Animating virtual avatar facial movements

ABSTRACT

Systems and methods generating an animation rig corresponding to a pose of a subject include accessing image data corresponding to the pose of the subject. The image data can include the face of the subject. The systems and methods process the image data by successively analyzing subregions of the image according to a solver order. The solver order can be biologically or anatomically ordered to proceed from subregions that cause larger scale movements to subregions that cause smaller scale movements. In each subregion, the systems and methods can perform an optimization technique to fit parameters of the animation rig to the input image data. After all subregions have been processed, the animation rig can be used to animate an avatar to appear to be performing the pose of the subject.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/643,548, filed Mar. 15, 2018, entitled “ANIMATING VIRTUAL AVATARFACIAL MOVEMENTS,” which is hereby incorporated by reference herein inits entirety.

COPYRIGHT STATEMENT

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The present disclosure relates to virtual reality and augmented reality,including mixed reality, imaging and visualization systems and moreparticularly to rigging systems and methods for animating virtualcharacters, such as avatars.

BACKGROUND

Modern computing and display technologies have facilitated thedevelopment of systems for so called “virtual reality,” “augmentedreality,” and “mixed reality” experiences, wherein digitally reproducedimages are presented to a user in a manner such that they seem to be, ormay be perceived as, real. A virtual reality (VR) scenario typicallyinvolves presentation of computer-generated virtual image informationwithout transparency to other actual real-world visual input. Anaugmented reality (AR) scenario typically involves presentation ofvirtual image information as an augmentation to visualization of theactual world around the user. Mixed reality (MR) is a type of augmentedreality in which physical and virtual objects may co-exist and interactin real time. Systems and methods disclosed herein address variouschallenges related to VR, AR and MR technology.

SUMMARY

Techniques for presenting an interactive virtual, augmented, or mixedreality environment that includes a high fidelity digital avatar aredescribed. For example, an avatar animation system can access an image(e.g., a high quality digitized photographic scan) of a subject and canconvert the image into a high fidelity digital avatar using facialmapping techniques that successively solve for facial subregions, ratherthan solving for all facial subregions simultaneously. The subject canbe a human, an animal, or other deformable object.

The Facial Action Coding System (FACS) classifies observable facialexpressions based on the appearance of a person's face by decomposingthe facial expressions into isolated muscle contractions or relaxations.Each isolated muscle contraction or relaxation of FACS is associatedwith a numerical representation, referred to as an Action Unit (AU). Insome embodiments, the disclosed systems and methods incorporate a facialtaxonomy into a facial rig (e.g., a digital puppet corresponding to thehuman subject) having facial rig parameters directly mapped to thefacial taxonomy. The facial taxonomy can (but need not) be based, atleast in part, on the FACS taxonomy and the facial rig parameters can(but need not) be based, at least in part, on FACS AUs. The facial rigparameters can be grouped into various facial subregions (e.g., Jaw,Lower Face, Lips, Funneler, Upper Face, Lids, Eyes, Neck, Lip Extras,Tongue, and Miscellaneous Extras).

For each facial subregion, some embodiments of the disclosed systemadjust various facial rig parameters of the particular facial subregionto reduce or minimize an error metric between the image of the subjectperforming a pose and the facial rig parameters of the particular facialsubregion. The system iteratively adjusts the facial rig parametersuntil a termination criterion is met (such as all of the parameters arewithin an acceptable threshold, as compared to the image or a maximumnumber of iteration steps has been performed). The output of the systemwhen fitting one facial subregion can be used as the input for asubsequent error minimization process for the next facial subregion.Accordingly, the system can work in a sequence that is mutuallyconstitutive. The sequence order (sometimes referred to as the SolverOrder) can flow from highest to lowest impact on fundamental facialmovements (e.g., marching in from large areas of movement, such as thejaw, and progressing to more granular details, such as the eyes). Onceeach of the facial subregions has been solved for, the system can setthe facial rig for an avatar corresponding to the image of the subject.The facial rig thereafter can be used to cause the avatar to display anappearance of the subject corresponding to the pose in the input dataimage.

In various advantageous embodiments, it is this process ofhierarchically compounding the results of the solution for each previoussubregion in the Solver Order and feeding that solution into the nextregion in the Solver Order that allows these embodiments of the systemto achieve a biologically motivated result that is qualitatively,quantitatively, or computationally robust.

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Neitherthis summary nor the following detailed description purports to defineor limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson.

FIG. 2 schematically illustrates an example of a wearable system.

FIG. 3 schematically illustrates example components of a wearablesystem.

FIG. 4 schematically illustrates an example of a waveguide stack of awearable device for outputting image information to a user.

FIG. 5 is a process flow diagram of an example of a method forinteracting with a virtual user interface.

FIG. 6A is a block diagram of another example of a wearable system whichcan comprise an avatar processing and rendering system.

FIG. 6B illustrates example components of an avatar processing andrendering system.

FIG. 7 is a block diagram of an example of a wearable system includingvarious inputs into the wearable system.

FIG. 8 is a process flow diagram of an example of a method of renderingvirtual content in relation to recognized objects.

FIG. 9A schematically illustrates an overall system view depictingmultiple wearable systems interacting with each other.

FIG. 9B illustrates an example telepresence session.

FIG. 10 illustrates an example of an avatar as perceived by a user of awearable system.

FIG. 11 illustrates an example of overfitting data.

FIG. 12 schematically illustrates an example of a facial action codingsystem (FACS) rig.

FIG. 13 schematically illustrates an example of a facial rig withoverlaid facial subregions. The Key provides an example of a SolverOrder based on these example facial subregions.

FIG. 14 is a block diagram that illustrates an example Facial Solverprocess.

FIG. 15 illustrates an example process for animating a virtual avatar.

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

DETAILED DESCRIPTION Overview

A virtual avatar may be a virtual representation of a real or fictionalperson (or creature or personified object) in an AR/VR/MR environment.Embodiments of the disclosed systems and methods may provide forimproved presentation of an interactive VR/AR/MR environment thatincludes a high fidelity digital avatar. Creation of a high fidelitydigital avatar can take many weeks or months of work by a specializedteam and can utilize a large number of high quality digitizedphotographic scans of the human model.

Embodiments of the disclosed technology have the capability of creatinghigh quality or high fidelity avatars (or digital representations ingeneral) for any human, animal, or other user. In order to accomplishthis, embodiments of the disclosed process are faster and less resourceintense (e.g., it is not practical to put users through the samescanning process a professional model may experience) while stillmaintaining an accurate output.

Facial Action Coding System (FACS) is a common system for determining orcharacterizing movements of human faces or emotions represented byfacial movements. FACS can comprise a system to taxonomize human facialmovements by their appearance on the face. For example, movements ofindividual facial muscles can be encoded by FACS from slightly differentchanges in facial appearance over short time intervals. Using FACS,nearly any anatomically possible facial expression can be coded, forexample, by deconstructing the expression into a specific Action Unit(AU) or Action Units. FACS defines AUs, which are a contraction orrelaxation of one or more muscles.

The disclosed systems and methods can incorporate the FACS taxonomy intoa methodology used by a Facial Solver as fixed boundaries on the humanface that are biologically or anatomically ordered (e.g., to representlarge scale facial movements down to smaller scale facial movements).The Facial Solver can be implemented as a hardware computing system thatcan access an input facial image (e.g., as point cloud data or a mesh)and execute instructions to adjust a facial animation rig for an avatarby iteratively solving over a sequence of subregions of the entire face.For example, the subregions of the entire face can comprise, the jawsubregion, the lower face subregion, the eyes subregions, the lipssubregion, and so forth. The Facial Solver sequence can be performed ina broad strokes approach, e.g., marching in from large areas ofmovement, such as the jaw, and progressing to more granular details,such as the eyes. The Facial Solver system can use this approach tosolve for the output avatar facial movements from arbitrary geometric orpoint cloud input data, which digitally represents facial movements(also referred to as poses) of a subject.

The input data representing the subject's facial movement (or movements)can be a three-dimensional (3D) digital representation of imaging scanstaken of the subject performing a pose or a sequence of poses. Theimaging scans can, for example, be taken by placing the subject in aphotogrammetry capture stage comprising multiple cameras (e.g., 60, 80,120, or more) surrounding and pointed at the subject. The cameras can besynchronized to each other to capture images that can be converted intoa 3D scan of the subject performing a pose. The 3D scan can be digitallyrepresented as a mesh of vertices, a point cloud (structured orunstructured), or any other type of representation (e.g., a color-depthimage).

Embodiments of the disclosed Facial Solver can convert one set of data(e.g., Data 1 comprising a user image that can be analyzed forunstructured point cloud data) to animation rig data (e.g., Data 2). TheFacial Solver can be applied to subregions of the input data andcalculate a minimum between Data 1 to Data 2 (e.g., rig format/data)iteratively over the subregions. For example, a first iteration startswith the rig at a neutral expression, and each iteration can adjust therig controls slightly until a threshold criterion is met when comparedto the input user image. The output from the first subregion'sminimization can be used as input for the second subregion, and theprocess can be repeated for all remaining subregions for the entireimage until all rig values (e.g., FACS AUs) are determined. The problemof quickly and accurately correlating disparate data (e.g., Data 1versus Data 2) can be solved by mechanics-motivated and Solver-orderedgroupings of facial rig parameters and/or subregions.

For example, as will be further described herein, the Facial Solver canproceed through an ordered set of facial subregions, first fittinglarger subregions of the face (e.g., starting with the jaw subregion)and moving on to successively smaller facial subregions (e.g., the eyes)to progressively match an avatar facial rig (e.g., FACS AUs) to an inputimage of the subject performing a pose (e.g., smiling, frowning,laughing, winking, etc.).

Considerable diagnostics, both quantitative and qualitative, have beenevaluated for an embodiment of the Facial Solver, and it has been foundthat the use of a facial taxonomy of fixed boundaries that arebiologically ordered outperforms a typical scalar minimization scheme.The facial taxonomy used by the Facial Solver may, but need not be,based on the FACS taxonomy. Advantageously, the results of the parameterminimization are biologically motivated and may be free from localminima artifacts.

Any of the embodiments of the Facial Solver described herein may beimplemented and performed by the avatar processing and rendering system690, for example, by the 3D model processing system 680. The FacialSolver may be implemented on computer hardware and, in someimplementations, the computer hardware may be in wired or wirelesscommunication with the photogrammetry capture stage. Although theexamples in this disclosure describe animating a human-shaped avatar,similar techniques can also be applied to animals, fictitious creatures,objects, etc.

Examples of 3D Display of a Wearable System

A wearable system (also referred to herein as an augmented reality (AR)system) can be configured to present 2D or 3D virtual images to a user.The images may be still images, frames of a video, or a video, incombination or the like. At least a portion of the wearable system canbe implemented on a wearable device that can present a VR, AR, or MRenvironment, alone or in combination, for user interaction. The wearabledevice can be used interchangeably as an AR device (ARD). Further, forthe purpose of the present disclosure, the term “AR” is usedinterchangeably with the term “MR”.

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson. In FIG. 1, an MR scene 100 is depicted wherein a user of an MRtechnology sees a real-world park-like setting 110 featuring people,trees, buildings in the background, and a concrete platform 120. Inaddition to these items, the user of the MR technology also perceivesthat he “sees” a robot statue 130 standing upon the real-world platform120, and a cartoon-like avatar character 140 flying by which seems to bea personification of a bumble bee, even though these elements do notexist in the real world.

In order for the 3D display to produce a true sensation of depth, andmore specifically, a simulated sensation of surface depth, it may bedesirable for each point in the display's visual field to generate anaccommodative response corresponding to its virtual depth. If theaccommodative response to a display point does not correspond to thevirtual depth of that point, as determined by the binocular depth cuesof convergence and stereopsis, the human eye may experience anaccommodation conflict, resulting in unstable imaging, harmful eyestrain, headaches, and, in the absence of accommodation information,almost a complete lack of surface depth.

VR, AR, and MR experiences can be provided by display systems havingdisplays in which images corresponding to a plurality of depth planesare provided to a viewer. The images may be different for each depthplane (e.g., provide slightly different presentations of a scene orobject) and may be separately focused by the viewer's eyes, therebyhelping to provide the user with depth cues based on the accommodationof the eye required to bring into focus different image features for thescene located on different depth plane or based on observing differentimage features on different depth planes being out of focus. Asdiscussed elsewhere herein, such depth cues provide credible perceptionsof depth.

FIG. 2 illustrates an example of wearable system 200 which can beconfigured to provide an AR/VR/MR scene. The wearable system 200 canalso be referred to as the AR system 200. The wearable system 200includes a display 220, and various mechanical and electronic modulesand systems to support the functioning of display 220. The display 220may be coupled to a frame 230, which is wearable by a user, wearer, orviewer 210. The display 220 can be positioned in font of the eyes of theuser 210. The display 220 can present AR/VR/MR content to a user. Thedisplay 220 can comprise a head mounted display (HMD) that is worn onthe head of the user.

In some embodiments, a speaker 240 is coupled to the frame 230 andpositioned adjacent the ear canal of the user (in some embodiments,another speaker, not shown, is positioned adjacent the other ear canalof the user to provide for stereo/shapeable sound control). The display220 can include an audio sensor (e.g., a microphone) 232 for detectingan audio stream from the environment and capture ambient sound. In someembodiments, one or more other audio sensors, not shown, are positionedto provide stereo sound reception. Stereo sound reception can be used todetermine the location of a sound source. The wearable system 200 canperform voice or speech recognition on the audio stream.

The wearable system 200 can include an outward-facing imaging system 464(shown in FIG. 4) which observes the world in the environment around theuser. The wearable system 200 can also include an inward-facing imagingsystem 462 (shown in FIG. 4) which can track the eye movements of theuser. The inward-facing imaging system may track either one eye'smovements or both eyes' movements. The inward-facing imaging system 462may be attached to the frame 230 and may be in electrical communicationwith the processing modules 260 or 270, which may process imageinformation acquired by the inward-facing imaging system to determine,e.g., the pupil diameters or orientations of the eyes, eye movements oreye pose of the user 210. The inward-facing imaging system 462 mayinclude one or more cameras. For example, at least one camera may beused to image each eye. The images acquired by the cameras may be usedto determine pupil size or eye pose for each eye separately, therebyallowing presentation of image information to each eye to be dynamicallytailored to that eye.

As an example, the wearable system 200 can use the outward-facingimaging system 464 or the inward-facing imaging system 462 to acquireimages of a pose of the user. The images may be still images, frames ofa video, or a video.

The display 220 can be operatively coupled 250, such as by a wired leador wireless connectivity, to a local data processing module 260 whichmay be mounted in a variety of configurations, such as fixedly attachedto the frame 230, fixedly attached to a helmet or hat worn by the user,embedded in headphones, or otherwise removably attached to the user 210(e.g., in a backpack-style configuration, in a belt-coupling styleconfiguration).

The local processing and data module 260 may comprise a hardwareprocessor, as well as digital memory, such as non-volatile memory (e.g.,flash memory), both of which may be utilized to assist in theprocessing, caching, and storage of data. The data may include data a)captured from sensors (which may be, e.g., operatively coupled to theframe 230 or otherwise attached to the user 210), such as image capturedevices (e.g., cameras in the inward-facing imaging system or theoutward-facing imaging system), audio sensors (e.g., microphones),inertial measurement units (IMUs), accelerometers, compasses, globalpositioning system (GPS) units, radio devices, or gyroscopes; or b)acquired or processed using remote processing module 270 or remote datarepository 280, possibly for passage to the display 220 after suchprocessing or retrieval. The local processing and data module 260 may beoperatively coupled by communication links 262 or 264, such as via wiredor wireless communication links, to the remote processing module 270 orremote data repository 280 such that these remote modules are availableas resources to the local processing and data module 260. In addition,remote processing module 280 and remote data repository 280 may beoperatively coupled to each other.

In some embodiments, the remote processing module 270 may comprise oneor more processors configured to analyze and process data or imageinformation. In some embodiments, the remote data repository 280 maycomprise a digital data storage facility, which may be available throughthe internet or other networking configuration in a “cloud” resourceconfiguration. In some embodiments, all data is stored and allcomputations are performed in the local processing and data module,allowing fully autonomous use from a remote module.

Example Components of a Wearable System

FIG. 3 schematically illustrates example components of a wearablesystem. FIG. 3 shows a wearable system 200 which can include a display220 and a frame 230. A blown-up view 202 schematically illustratesvarious components of the wearable system 200. In certain implements,one or more of the components illustrated in FIG. 3 can be part of thedisplay 220. The various components alone or in combination can collecta variety of data (such as e.g., audio or visual data) associated withthe user of the wearable system 200 or the user's environment. It shouldbe appreciated that other embodiments may have additional or fewercomponents depending on the application for which the wearable system isused. Nevertheless, FIG. 3 provides a basic idea of some of the variouscomponents and types of data that may be collected, analyzed, and storedthrough the wearable system.

FIG. 3 shows an example wearable system 200 which can include thedisplay 220. The display 220 can comprise a display lens 226 that may bemounted to a user's head or a housing or frame 230, which corresponds tothe frame 230. The display lens 226 may comprise one or more transparentmirrors positioned by the housing 230 in front of the user's eyes 302,304 and may be configured to bounce projected light 338 into the eyes302, 304 and facilitate beam shaping, while also allowing fortransmission of at least some light from the local environment. Thewavefront of the projected light beam 338 may be bent or focused tocoincide with a desired focal distance of the projected light. Asillustrated, two wide-field-of-view machine vision cameras 316 (alsoreferred to as world cameras) can be coupled to the housing 230 to imagethe environment around the user. These cameras 316 can be dual capturevisible light/non-visible (e.g., infrared) light cameras. The cameras316 may be part of the outward-facing imaging system 464 shown in FIG.4. Image acquired by the world cameras 316 can be processed by the poseprocessor 336. For example, the pose processor 336 can implement one ormore object recognizers 708 (e.g., shown in FIG. 7) to identify a poseof a user or another person in the user's environment or to identify aphysical object in the user's environment.

With continued reference to FIG. 3, a pair of scanned-lasershaped-wavefront (e.g., for depth) light projector modules with displaymirrors and optics configured to project light 338 into the eyes 302,304 are shown. The depicted view also shows two miniature infraredcameras 324 paired with infrared light (such as light emitting diodes“LED”s), which are configured to be able to track the eyes 302, 304 ofthe user to support rendering and user input. The cameras 324 may bepart of the inward-facing imaging system 462 shown in FIG. 4 Thewearable system 200 can further feature a sensor assembly 339, which maycomprise X, Y, and Z axis accelerometer capability as well as a magneticcompass and X, Y, and Z axis gyro capability, preferably providing dataat a relatively high frequency, such as 200 Hz. The sensor assembly 339may be part of the IMU described with reference to FIG. 2A The depictedsystem 200 can also comprise a head pose processor 336, such as an ASIC(application specific integrated circuit), FPGA (field programmable gatearray), or ARM processor (advanced reduced-instruction-set machine),which may be configured to calculate real or near-real time user headpose from wide field of view image information output from the capturedevices 316. The head pose processor 336 can be a hardware processor andcan be implemented as part of the local processing and data module 260shown in FIG. 2A.

The wearable system can also include one or more depth sensors 234. Thedepth sensor 234 can be configured to measure the distance between anobject in an environment to a wearable device. The depth sensor 234 mayinclude a laser scanner (e.g., a lidar), an ultrasonic depth sensor, ora depth sensing camera. In certain implementations, where the cameras316 have depth sensing ability, the cameras 316 may also be consideredas depth sensors 234.

Also shown is a processor 332 configured to execute digital or analogprocessing to derive pose from the gyro, compass, or accelerometer datafrom the sensor assembly 339. The processor 332 may be part of the localprocessing and data module 260 shown in FIG. 2. The wearable system 200as shown in FIG. 3 can also include a position system such as, e.g., aGPS 337 (global positioning system) to assist with pose and positioninganalyses. In addition, the GPS may further provide remotely-based (e.g.,cloud-based) information about the user's environment. This informationmay be used for recognizing objects or information in user'senvironment.

The wearable system may combine data acquired by the GPS 337 and aremote computing system (such as, e.g., the remote processing module270, another user's ARD, etc.) which can provide more information aboutthe user's environment. As one example, the wearable system candetermine the user's location based on GPS data and retrieve a world map(e.g., by communicating with a remote processing module 270) includingvirtual objects associated with the user's location. As another example,the wearable system 200 can monitor the environment using the worldcameras 316 (which may be part of the outward-facing imaging system 464shown in FIG. 4). Based on the images acquired by the world cameras 316,the wearable system 200 can detect objects in the environment (e.g., byusing one or more object recognizers 708 shown in FIG. 7). The wearablesystem can further use data acquired by the GPS 337 to interpret thecharacters.

The wearable system 200 may also comprise a rendering engine 334 whichcan be configured to provide rendering information that is local to theuser to facilitate operation of the scanners and imaging into the eyesof the user, for the user's view of the world. The rendering engine 334may be implemented by a hardware processor (such as, e.g., a centralprocessing unit or a graphics processing unit). In some embodiments, therendering engine is part of the local processing and data module 260.The rendering engine 334 can be communicatively coupled (e.g., via wiredor wireless links) to other components of the wearable system 200. Forexample, the rendering engine 334, can be coupled to the eye cameras 324via communication link 274, and be coupled to a projecting subsystem 318(which can project light into user's eyes 302, 304 via a scanned laserarrangement in a manner similar to a retinal scanning display) via thecommunication link 272. The rendering engine 334 can also be incommunication with other processing units such as, e.g., the sensor poseprocessor 332 and the image pose processor 336 via links 276 and 294respectively.

The cameras 324 (e.g., mini infrared cameras) may be utilized to trackthe eye pose to support rendering and user input. Some example eye posesmay include where the user is looking or at what depth he or she isfocusing (which may be estimated with eye vergence). The GPS 337, gyros,compass, and accelerometers 339 may be utilized to provide coarse orfast pose estimates. One or more of the cameras 316 can acquire imagesand pose, which in conjunction with data from an associated cloudcomputing resource, may be utilized to map the local environment andshare user views with others.

The example components depicted in FIG. 3 are for illustration purposesonly. Multiple sensors and other functional modules are shown togetherfor ease of illustration and description. Some embodiments may includeonly one or a subset of these sensors or modules. Further, the locationsof these components are not limited to the positions depicted in FIG. 3.Some components may be mounted to or housed within other components,such as a belt-mounted component, a hand-held component, or a helmetcomponent. As one example, the image pose processor 336, sensor poseprocessor 332, and rendering engine 334 may be positioned in a beltpackand configured to communicate with other components of the wearablesystem via wireless communication, such as ultra-wideband, Wi-Fi,Bluetooth, etc., or via wired communication. The depicted housing 230preferably is head-mountable and wearable by the user. However, somecomponents of the wearable system 200 may be worn to other portions ofthe user's body. For example, the speaker 240 may be inserted into theears of a user to provide sound to the user.

Regarding the projection of light 338 into the eyes 302, 304 of theuser, in some embodiment, the cameras 324 may be utilized to measurewhere the centers of a user's eyes are geometrically verged to, which,in general, coincides with a position of focus, or “depth of focus”, ofthe eyes. A 3-dimensional surface of all points the eyes verge to can bereferred to as the “horopter”. The focal distance may take on a finitenumber of depths, or may be infinitely varying. Light projected from thevergence distance appears to be focused to the subject eye 302, 304,while light in front of or behind the vergence distance is blurred.Examples of wearable devices and other display systems of the presentdisclosure are also described in U.S. Patent Publication No.2016/0270656, which is incorporated by reference herein in its entirety.

The human visual system is complicated and providing a realisticperception of depth is challenging. Viewers of an object may perceivethe object as being three-dimensional due to a combination of vergenceand accommodation. Vergence movements (e.g., rolling movements of thepupils toward or away from each other to converge the lines of sight ofthe eyes to fixate upon an object) of the two eyes relative to eachother are closely associated with focusing (or “accommodation”) of thelenses of the eyes. Under normal conditions, changing the focus of thelenses of the eyes, or accommodating the eyes, to change focus from oneobject to another object at a different distance will automaticallycause a matching change in vergence to the same distance, under arelationship known as the “accommodation-vergence reflex.” Likewise, achange in vergence will trigger a matching change in accommodation,under normal conditions. Display systems that provide a better matchbetween accommodation and vergence may form more realistic andcomfortable simulations of three-dimensional imagery.

Further spatially coherent light with a beam diameter of less than about0.7 millimeters can be correctly resolved by the human eye regardless ofwhere the eye focuses. Thus, to create an illusion of proper focaldepth, the eye vergence may be tracked with the cameras 324, and therendering engine 334 and projection subsystem 318 may be utilized torender all objects on or close to the horopter in focus, and all otherobjects at varying degrees of defocus (e.g., using intentionally-createdblurring). Preferably, the system 220 renders to the user at a framerate of about 60 frames per second or greater. As described above,preferably, the cameras 324 may be utilized for eye tracking, andsoftware may be configured to pick up not only vergence geometry butalso focus location cues to serve as user inputs. Preferably, such adisplay system is configured with brightness and contrast suitable forday or night use.

In some embodiments, the display system preferably has latency of lessthan about 20 milliseconds for visual object alignment, less than about0.1 degree of angular alignment, and about 1 arc minute of resolution,which, without being limited by theory, is believed to be approximatelythe limit of the human eye. The display system 220 may be integratedwith a localization system, which may involve GPS elements, opticaltracking, compass, accelerometers, or other data sources, to assist withposition and pose determination; localization information may beutilized to facilitate accurate rendering in the user's view of thepertinent world (e.g., such information would facilitate the glasses toknow where they are with respect to the real world).

In some embodiments, the wearable system 200 is configured to displayone or more virtual images based on the accommodation of the user'seyes. Unlike prior 3D display approaches that force the user to focuswhere the images are being projected, in some embodiments, the wearablesystem is configured to automatically vary the focus of projectedvirtual content to allow for a more comfortable viewing of one or moreimages presented to the user. For example, if the user's eyes have acurrent focus of 1 m, the image may be projected to coincide with theuser's focus. If the user shifts focus to 3 m, the image is projected tocoincide with the new focus. Thus, rather than forcing the user to apredetermined focus, the wearable system 200 of some embodiments allowsthe user's eye to a function in a more natural manner.

Such a wearable system 200 may eliminate or reduce the incidences of eyestrain, headaches, and other physiological symptoms typically observedwith respect to virtual reality devices. To achieve this, variousembodiments of the wearable system 200 are configured to project virtualimages at varying focal distances, through one or more variable focuselements (VFEs). In one or more embodiments, 3D perception may beachieved through a multi-plane focus system that projects images atfixed focal planes away from the user. Other embodiments employ variableplane focus, wherein the focal plane is moved back and forth in thez-direction to coincide with the user's present state of focus.

In both the multi-plane focus systems and variable plane focus systems,wearable system 200 may employ eye tracking to determine a vergence ofthe user's eyes, determine the user's current focus, and project thevirtual image at the determined focus. In other embodiments, wearablesystem 200 comprises a light modulator that variably projects, through afiber scanner, or other light generating source, light beams of varyingfocus in a raster pattern across the retina. Thus, the ability of thedisplay of the wearable system 200 to project images at varying focaldistances not only eases accommodation for the user to view objects in3D, but may also be used to compensate for user ocular anomalies, asfurther described in U.S. Patent Publication No. 2016/0270656, which isincorporated by reference herein in its entirety. In some otherembodiments, a spatial light modulator may project the images to theuser through various optical components. For example, as describedfurther below, the spatial light modulator may project the images ontoone or more waveguides, which then transmit the images to the user.

Waveguide Stack Assembly

FIG. 4 illustrates an example of a waveguide stack for outputting imageinformation to a user. A wearable system 400 includes a stack ofwaveguides, or stacked waveguide assembly 480 that may be utilized toprovide three-dimensional perception to the eye/brain using a pluralityof waveguides 432 b, 434 b, 436 b, 438 b, 4400 b. In some embodiments,the wearable system 400 may correspond to wearable system 200 of FIG. 2,with FIG. 4 schematically showing some parts of that wearable system 200in greater detail. For example, in some embodiments, the waveguideassembly 480 may be integrated into the display 220 of FIG. 2.

With continued reference to FIG. 4, the waveguide assembly 480 may alsoinclude a plurality of features 458, 456, 454, 452 between thewaveguides. In some embodiments, the features 458, 456, 454, 452 may belenses. In other embodiments, the features 458, 456, 454, 452 may not belenses. Rather, they may simply be spacers (e.g., cladding layers orstructures for forming air gaps).

The waveguides 432 b, 434 b, 436 b, 438 b, 440 b or the plurality oflenses 458, 456, 454, 452 may be configured to send image information tothe eye with various levels of wavefront curvature or light raydivergence. Each waveguide level may be associated with a particulardepth plane and may be configured to output image informationcorresponding to that depth plane. Image injection devices 420, 422,424, 426, 428 may be utilized to inject image information into thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b, each of which may beconfigured to distribute incoming light across each respectivewaveguide, for output toward the eye 410. Light exits an output surfaceof the image injection devices 420, 422, 424, 426, 428 and is injectedinto a corresponding input edge of the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, a single beam of light (e.g., acollimated beam) may be injected into each waveguide to output an entirefield of cloned collimated beams that are directed toward the eye 410 atparticular angles (and amounts of divergence) corresponding to the depthplane associated with a particular waveguide.

In some embodiments, the image injection devices 420, 422, 424, 426, 428are discrete displays that each produce image information for injectioninto a corresponding waveguide 440 b, 438 b, 436 b, 434 b, 432 b,respectively. In some other embodiments, the image injection devices420, 422, 424, 426, 428 are the output ends of a single multiplexeddisplay which may, e.g., pipe image information via one or more opticalconduits (such as fiber optic cables) to each of the image injectiondevices 420, 422, 424, 426, 428.

A controller 460 controls the operation of the stacked waveguideassembly 480 and the image injection devices 420, 422, 424, 426, 428.The controller 460 includes programming (e.g., instructions in anon-transitory computer-readable medium) that regulates the timing andprovision of image information to the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, the controller 460 may be a singleintegral device, or a distributed system connected by wired or wirelesscommunication channels. The controller 460 may be part of the processingmodules 260 or 270 (illustrated in FIG. 2) in some embodiments.

The waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be configured topropagate light within each respective waveguide by total internalreflection (TIR). The waveguides 440 b, 438 b, 436 b, 434 b, 432 b mayeach be planar or have another shape (e.g., curved), with major top andbottom surfaces and edges extending between those major top and bottomsurfaces. In the illustrated configuration, the waveguides 440 b, 438 b,436 b, 434 b, 432 b may each include light extracting optical elements440 a, 438 a, 436 a, 434 a, 432 a that are configured to extract lightout of a waveguide by redirecting the light, propagating within eachrespective waveguide, out of the waveguide to output image informationto the eye 410. Extracted light may also be referred to as outcoupledlight, and light extracting optical elements may also be referred to asoutcoupling optical elements. An extracted beam of light is outputted bythe waveguide at locations at which the light propagating in thewaveguide strikes a light redirecting element. The light extractingoptical elements (440 a, 438 a, 436 a, 434 a, 432 a) may, for example,be reflective or diffractive optical features. While illustrateddisposed at the bottom major surfaces of the waveguides 440 b, 438 b,436 b, 434 b, 432 b for ease of description and drawing clarity, in someembodiments, the light extracting optical elements 440 a, 438 a, 436 a,434 a, 432 a may be disposed at the top or bottom major surfaces, or maybe disposed directly in the volume of the waveguides 440 b, 438 b, 436b, 434 b, 432 b. In some embodiments, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be formed in a layer ofmaterial that is attached to a transparent substrate to form thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some other embodiments,the waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be a monolithicpiece of material and the light extracting optical elements 440 a, 438a, 436 a, 434 a, 432 a may be formed on a surface or in the interior ofthat piece of material.

With continued reference to FIG. 4, as discussed herein, each waveguide440 b, 438 b, 436 b, 434 b, 432 b is configured to output light to forman image corresponding to a particular depth plane. For example, thewaveguide 432 b nearest the eye may be configured to deliver collimatedlight, as injected into such waveguide 432 b, to the eye 410. Thecollimated light may be representative of the optical infinity focalplane. The next waveguide up 434 b may be configured to send outcollimated light which passes through the first lens 452 (e.g., anegative lens) before it can reach the eye 410. First lens 452 may beconfigured to create a slight convex wavefront curvature so that theeye/brain interprets light coming from that next waveguide up 434 b ascoming from a first focal plane closer inward toward the eye 410 fromoptical infinity. Similarly, the third up waveguide 436 b passes itsoutput light through both the first lens 452 and second lens 454 beforereaching the eye 410. The combined optical power of the first and secondlenses 452 and 454 may be configured to create another incrementalamount of wavefront curvature so that the eye/brain interprets lightcoming from the third waveguide 436 b as coming from a second focalplane that is even closer inward toward the person from optical infinitythan was light from the next waveguide up 434 b.

The other waveguide layers (e.g., waveguides 438 b, 440 b) and lenses(e.g., lenses 456, 458) are similarly configured, with the highestwaveguide 440 b in the stack sending its output through all of thelenses between it and the eye for an aggregate focal powerrepresentative of the closest focal plane to the person. To compensatefor the stack of lenses 458, 456, 454, 452 when viewing/interpretinglight coming from the world 470 on the other side of the stackedwaveguide assembly 480, a compensating lens layer 430 may be disposed atthe top of the stack to compensate for the aggregate power of the lensstack 458, 456, 454, 452 below. Such a configuration provides as manyperceived focal planes as there are available waveguide/lens pairings.Both the light extracting optical elements of the waveguides and thefocusing aspects of the lenses may be static (e.g., not dynamic orelectro-active). In some alternative embodiments, either or both may bedynamic using electro-active features.

With continued reference to FIG. 4, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be configured to bothredirect light out of their respective waveguides and to output thislight with the appropriate amount of divergence or collimation for aparticular depth plane associated with the waveguide. As a result,waveguides having different associated depth planes may have differentconfigurations of light extracting optical elements, which output lightwith a different amount of divergence depending on the associated depthplane. In some embodiments, as discussed herein, the light extractingoptical elements 440 a, 438 a, 436 a, 434 a, 432 a may be volumetric orsurface features, which may be configured to output light at specificangles. For example, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a may be volume holograms, surface holograms, and/ordiffraction gratings. Light extracting optical elements, such asdiffraction gratings, are described in U.S. Patent Publication No.2015/0178939, published Jun. 25, 2015, which is incorporated byreference herein in its entirety.

In some embodiments, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a are diffractive features that form a diffractionpattern, or “diffractive optical element” (also referred to herein as a“DOE”). Preferably, the DOE has a relatively low diffraction efficiencyso that only a portion of the light of the beam is deflected away towardthe eye 410 with each intersection of the DOE, while the rest continuesto move through a waveguide via total internal reflection. The lightcarrying the image information can thus be divided into a number ofrelated exit beams that exit the waveguide at a multiplicity oflocations and the result is a fairly uniform pattern of exit emissiontoward the eye 304 for this particular collimated beam bouncing aroundwithin a waveguide.

In some embodiments, one or more DOEs may be switchable between “on”state in which they actively diffract, and “off” state in which they donot significantly diffract. For instance, a switchable DOE may comprisea layer of polymer dispersed liquid crystal, in which microdropletscomprise a diffraction pattern in a host medium, and the refractiveindex of the microdroplets can be switched to substantially match therefractive index of the host material (in which case the pattern doesnot appreciably diffract incident light) or the microdroplet can beswitched to an index that does not match that of the host medium (inwhich case the pattern actively diffracts incident light).

In some embodiments, the number and distribution of depth planes ordepth of field may be varied dynamically based on the pupil sizes ororientations of the eyes of the viewer. Depth of field may changeinversely with a viewer's pupil size. As a result, as the sizes of thepupils of the viewer's eyes decrease, the depth of field increases suchthat one plane that is not discernible because the location of thatplane is beyond the depth of focus of the eye may become discernible andappear more in focus with reduction of pupil size and commensurate withthe increase in depth of field. Likewise, the number of spaced apartdepth planes used to present different images to the viewer may bedecreased with the decreased pupil size. For example, a viewer may notbe able to clearly perceive the details of both a first depth plane anda second depth plane at one pupil size without adjusting theaccommodation of the eye away from one depth plane and to the otherdepth plane. These two depth planes may, however, be sufficiently infocus at the same time to the user at another pupil size withoutchanging accommodation.

In some embodiments, the display system may vary the number ofwaveguides receiving image information based upon determinations ofpupil size or orientation, or upon receiving electrical signalsindicative of particular pupil size or orientation. For example, if theuser's eyes are unable to distinguish between two depth planesassociated with two waveguides, then the controller 460 (which may be anembodiment of the local processing and data module 260) can beconfigured or programmed to cease providing image information to one ofthese waveguides. Advantageously, this may reduce the processing burdenon the system, thereby increasing the responsiveness of the system. Inembodiments in which the DOEs for a waveguide are switchable between theon and off states, the DOEs may be switched to the off state when thewaveguide does receive image information.

In some embodiments, it may be desirable to have an exit beam meet thecondition of having a diameter that is less than the diameter of the eyeof a viewer. However, meeting this condition may be challenging in viewof the variability in size of the viewer's pupils. In some embodiments,this condition is met over a wide range of pupil sizes by varying thesize of the exit beam in response to determinations of the size of theviewer's pupil. For example, as the pupil size decreases, the size ofthe exit beam may also decrease. In some embodiments, the exit beam sizemay be varied using a variable aperture.

The wearable system 400 can include an outward-facing imaging system 464(e.g., a digital camera) that images a portion of the world 470. Thisportion of the world 470 may be referred to as the field of view (FOV)of a world camera and the imaging system 464 is sometimes referred to asan FOV camera. The FOV of the world camera may or may not be the same asthe FOV of a viewer 210 which encompasses a portion of the world 470 theviewer 210 perceives at a given time. For example, in some situations,the FOV of the world camera may be larger than the viewer 210 of theviewer 210 of the wearable system 400. The entire subregion availablefor viewing or imaging by a viewer may be referred to as the field ofregard (FOR). The FOR may include 4π steradians of solid anglesurrounding the wearable system 400 because the wearer can move hisbody, head, or eyes to perceive substantially any direction in space. Inother contexts, the wearer's movements may be more constricted, andaccordingly the wearer's FOR may subtend a smaller solid angle. Imagesobtained from the outward-facing imaging system 464 can be used to trackgestures made by the user (e.g., hand or finger gestures), detectobjects in the world 470 in front of the user, and so forth.

The wearable system 400 can include an audio sensor 232, e.g., amicrophone, to capture ambient sound. As described above, in someembodiments, one or more other audio sensors can be positioned toprovide stereo sound reception useful to the determination of locationof a speech source. The audio sensor 232 can comprise a directionalmicrophone, as another example, which can also provide such usefuldirectional information as to where the audio source is located. Thewearable system 400 can use information from both the outward-facingimaging system 464 and the audio sensor 230 in locating a source ofspeech, or to determine an active speaker at a particular moment intime, etc. For example, the wearable system 400 can use the voicerecognition alone or in combination with a reflected image of thespeaker (e.g., as seen in a mirror) to determine the identity of thespeaker. As another example, the wearable system 400 can determine aposition of the speaker in an environment based on sound acquired fromdirectional microphones. The wearable system 400 can parse the soundcoming from the speaker's position with speech recognition algorithms todetermine the content of the speech and use voice recognition techniquesto determine the identity (e.g., name or other demographic information)of the speaker.

The wearable system 400 can also include an inward-facing imaging system466 (e.g., a digital camera), which observes the movements of the user,such as the eye movements and the facial movements. The inward-facingimaging system 466 may be used to capture images of the eye 410 todetermine the size and/or orientation of the pupil of the eye 304. Theinward-facing imaging system 466 can be used to obtain images for use indetermining the direction the user is looking (e.g., eye pose) or forbiometric identification of the user (e.g., via iris identification). Insome embodiments, at least one camera may be utilized for each eye, toseparately determine the pupil size or eye pose of each eyeindependently, thereby allowing the presentation of image information toeach eye to be dynamically tailored to that eye. In some otherembodiments, the pupil diameter or orientation of only a single eye 410(e.g., using only a single camera per pair of eyes) is determined andassumed to be similar for both eyes of the user. The images obtained bythe inward-facing imaging system 466 may be analyzed to determine theuser's eye pose or mood, which can be used by the wearable system 400 todecide which audio or visual content should be presented to the user.The wearable system 400 may also determine head pose (e.g., headposition or head orientation) using sensors such as IMUs,accelerometers, gyroscopes, etc.

The wearable system 400 can include a user input device 466 by which theuser can input commands to the controller 460 to interact with thewearable system 400. For example, the user input device 466 can includea trackpad, a touchscreen, a joystick, a multiple degree-of-freedom(DOF) controller, a capacitive sensing device, a game controller, akeyboard, a mouse, a directional pad (D-pad), a wand, a haptic device, atotem (e.g., functioning as a virtual user input device), and so forth.A multi-DOF controller can sense user input in some or all possibletranslations (e.g., left/right, forward/backward, or up/down) orrotations (e.g., yaw, pitch, or roll) of the controller. A multi-DOFcontroller which supports the translation movements may be referred toas a 3DOF while a multi-DOF controller which supports the translationsand rotations may be referred to as 6DOF. In some cases, the user mayuse a finger (e.g., a thumb) to press or swipe on a touch-sensitiveinput device to provide input to the wearable system 400 (e.g., toprovide user input to a user interface provided by the wearable system400). The user input device 466 may be held by the user's hand duringthe use of the wearable system 400. The user input device 466 can be inwired or wireless communication with the wearable system 400.

Other Components of the Wearable System

In many implementations, the wearable system may include othercomponents in addition or in alternative to the components of thewearable system described above. The wearable system may, for example,include one or more haptic devices or components. The haptic devices orcomponents may be operable to provide a tactile sensation to a user. Forexample, the haptic devices or components may provide a tactilesensation of pressure or texture when touching virtual content (e.g.,virtual objects, virtual tools, other virtual constructs). The tactilesensation may replicate a feel of a physical object which a virtualobject represents, or may replicate a feel of an imagined object orcharacter (e.g., a dragon) which the virtual content represents. In someimplementations, haptic devices or components may be worn by the user(e.g., a user wearable glove). In some implementations, haptic devicesor components may be held by the user.

The wearable system may, for example, include one or more physicalobjects which are manipulable by the user to allow input or interactionwith the wearable system. These physical objects may be referred toherein as totems. Some totems may take the form of inanimate objects,such as for example, a piece of metal or plastic, a wall, a surface oftable. In certain implementations, the totems may not actually have anyphysical input structures (e.g., keys, triggers, joystick, trackball,rocker switch). Instead, the totem may simply provide a physicalsurface, and the wearable system may render a user interface so as toappear to a user to be on one or more surfaces of the totem. Forexample, the wearable system may render an image of a computer keyboardand trackpad to appear to reside on one or more surfaces of a totem. Forexample, the wearable system may render a virtual computer keyboard andvirtual trackpad to appear on a surface of a thin rectangular plate ofaluminum which serves as a totem. The rectangular plate does not itselfhave any physical keys or trackpad or sensors. However, the wearablesystem may detect user manipulation or interaction or touches with therectangular plate as selections or inputs made via the virtual keyboardor virtual trackpad. The user input device 466 (shown in FIG. 4) may bean embodiment of a totem, which may include a trackpad, a touchpad, atrigger, a joystick, a trackball, a rocker or virtual switch, a mouse, akeyboard, a multi-degree-of-freedom controller, or another physicalinput device. A user may use the totem, alone or in combination withposes, to interact with the wearable system or other users.

Examples of haptic devices and totems usable with the wearable devices,HMD, and display systems of the present disclosure are described in U.S.Patent Publication No. 2015/0016777, which is incorporated by referenceherein in its entirety.

Example Processes of User Interactions with a Wearable System

FIG. 5 is a process flow diagram of an example of a method 500 forinteracting with a virtual user interface. The method 500 may beperformed by the wearable system described herein. Embodiments of themethod 500 can be used by the wearable system to detect persons ordocuments in the FOV of the wearable system.

At block 510, the wearable system may identify a particular UI. The typeof UI may be predetermined by the user. The wearable system may identifythat a particular UI needs to be populated based on a user input (e.g.,gesture, visual data, audio data, sensory data, direct command, etc.).The UI can be specific to a security scenario where the wearer of thesystem is observing users who present documents to the wearer (e.g., ata travel checkpoint). At block 520, the wearable system may generatedata for the virtual UI. For example, data associated with the confines,general structure, shape of the UI etc., may be generated. In addition,the wearable system may determine map coordinates of the user's physicallocation so that the wearable system can display the UI in relation tothe user's physical location. For example, if the UI is body centric,the wearable system may determine the coordinates of the user's physicalstance, head pose, or eye pose such that a ring UI can be displayedaround the user or a planar UI can be displayed on a wall or in front ofthe user. In the security context described herein, the UI may bedisplayed as if the UI were surrounding the traveler who is presentingdocuments to the wearer of the system, so that the wearer can readilyview the UI while looking at the traveler and the traveler's documents.If the UI is hand centric, the map coordinates of the user's hands maybe determined. These map points may be derived through data receivedthrough the FOV cameras, sensory input, or any other type of collecteddata.

At block 530, the wearable system may send the data to the display fromthe cloud or the data may be sent from a local database to the displaycomponents. At block 540, the UI is displayed to the user based on thesent data. For example, a light field display can project the virtual UIinto one or both of the user's eyes. Once the virtual UI has beencreated, the wearable system may simply wait for a command from the userto generate more virtual content on the virtual UI at block 550. Forexample, the UI may be a body centric ring around the user's body or thebody of a person in the user's environment (e.g., a traveler). Thewearable system may then wait for the command (a gesture, a head or eyemovement, voice command, input from a user input device, etc.), and ifit is recognized (block 560), virtual content associated with thecommand may be displayed to the user (block 570).

Examples of Avatar Rendering in Mixed Reality

A wearable system may employ various mapping related techniques in orderto achieve high depth of field in the rendered light fields. In mappingout the virtual world, it is advantageous to know all the features andpoints in the real world to accurately portray virtual objects inrelation to the real world. To this end, FOV images captured from usersof the wearable system can be added to a world model by including newpictures that convey information about various points and features ofthe real world. For example, the wearable system can collect a set ofmap points (such as 2D points or 3D points) and find new map points torender a more accurate version of the world model. The world model of afirst user can be communicated (e.g., over a network such as a cloudnetwork) to a second user so that the second user can experience theworld surrounding the first user.

FIG. 6A is a block diagram of another example of a wearable system whichcan comprise an avatar processing and rendering system 690 in a mixedreality environment. The wearable system 600 may be part of the wearablesystem 200 shown in FIG. 2. In this example, the wearable system 600 cancomprise a map 620, which may include at least a portion of the data inthe map database 710 (shown in FIG. 7). The map may partly residelocally on the wearable system, and may partly reside at networkedstorage locations accessible by wired or wireless network (e.g., in acloud system). A pose process 610 may be executed on the wearablecomputing architecture (e.g., processing module 260 or controller 460)and utilize data from the map 620 to determine position and orientationof the wearable computing hardware or user. Pose data may be computedfrom data collected on the fly as the user is experiencing the systemand operating in the world. The data may comprise images, data fromsensors (such as inertial measurement units, which generally compriseaccelerometer and gyroscope components) and surface informationpertinent to objects in the real or virtual environment.

A sparse point representation may be the output of a simultaneouslocalization and mapping (e.g., SLAM or vSLAM, referring to aconfiguration wherein the input is images/visual only) process. Thesystem can be configured to not only find out where in the world thevarious components are, but what the world is made of. Pose may be abuilding block that achieves many goals, including populating the mapand using the data from the map.

In one embodiment, a sparse point position may not be completelyadequate on its own, and further information may be needed to produce amultifocal AR, VR, or MR experience. Dense representations, generallyreferring to depth map information, may be utilized to fill this gap atleast in part. Such information may be computed from a process referredto as Stereo 640, wherein depth information is determined using atechnique such as triangulation or time-of-flight sensing. Imageinformation and active patterns (such as infrared patterns created usingactive projectors), images acquired from image cameras, or handgestures/totem 650 may serve as input to the Stereo process 640. Asignificant amount of depth map information may be fused together, andsome of this may be summarized with a surface representation. Forexample, mathematically definable surfaces may be efficient (e.g.,relative to a large point cloud) and digestible inputs to otherprocessing devices like game engines. Thus, the output of the stereoprocess (e.g., a depth map) 640 may be combined in the fusion process630. Pose 610 may be an input to this fusion process 630 as well, andthe output of fusion 630 becomes an input to populating the map process620. Sub-surfaces may connect with each other, such as in topographicalmapping, to form larger surfaces, and the map becomes a large hybrid ofpoints and surfaces.

To resolve various aspects in a mixed reality process 660, variousinputs may be utilized. For example, in the embodiment depicted in FIG.6A, Game parameters may be inputs to determine that the user of thesystem is playing a monster battling game with one or more monsters atvarious locations, monsters dying or running away under variousconditions (such as if the user shoots the monster), walls or otherobjects at various locations, and the like. The world map may includeinformation regarding the location of the objects or semanticinformation of the objects (e.g., classifications such as whether theobject is flat or round, horizontal or vertical, a table or a lamp,etc.) and the world map can be another valuable input to mixed reality.Pose relative to the world becomes an input as well and plays a key roleto almost any interactive system.

Controls or inputs from the user are another input to the wearablesystem 600. As described herein, user inputs can include visual input,gestures, totems, audio input, sensory input, etc. In order to movearound or play a game, for example, the user may need to instruct thewearable system 600 regarding what he or she wants to do. Beyond justmoving oneself in space, there are various forms of user controls thatmay be utilized. In one embodiment, a totem (e.g. a user input device),or an object such as a toy gun may be held by the user and tracked bythe system. The system preferably will be configured to know that theuser is holding the item and understand what kind of interaction theuser is having with the item (e.g., if the totem or object is a gun, thesystem may be configured to understand location and orientation, as wellas whether the user is clicking a trigger or other sensed button orelement which may be equipped with a sensor, such as an IMU, which mayassist in determining what is going on, even when such activity is notwithin the field of view of any of the cameras.)

Hand gesture tracking or recognition may also provide input information.The wearable system 600 may be configured to track and interpret handgestures for button presses, for gesturing left or right, stop, grab,hold, etc. For example, in one configuration, the user may want to flipthrough emails or a calendar in a non-gaming environment, or do a “fistbump” with another person or player. The wearable system 600 may beconfigured to leverage a minimum amount of hand gesture, which may ormay not be dynamic. For example, the gestures may be simple staticgestures like open hand for stop, thumbs up for ok, thumbs down for notok; or a hand flip right, or left, or up/down for directional commands.

Eye tracking is another input (e.g., tracking where the user is lookingto control the display technology to render at a specific depth orrange). In one embodiment, vergence of the eyes may be determined usingtriangulation, and then using a vergence/accommodation model developedfor that particular person, accommodation may be determined. Eyetracking can be performed by the eye camera(s) to determine eye gaze(e.g., direction or orientation of one or both eyes). Other techniquescan be used for eye tracking such as, e.g., measurement of electricalpotentials by electrodes placed near the eye(s) (e.g.,electrooculography).

Speech tracking can be another input can be used alone or in combinationwith other inputs (e.g., totem tracking, eye tracking, gesture tracking,etc.). Speech tracking may include speech recognition, voicerecognition, alone or in combination. The system 600 can include anaudio sensor (e.g., a microphone) that receives an audio stream from theenvironment. The system 600 can incorporate voice recognition technologyto determine who is speaking (e.g., whether the speech is from thewearer of the ARD or another person or voice (e.g., a recorded voicetransmitted by a loudspeaker in the environment)) as well as speechrecognition technology to determine what is being said. The local data &processing module 260 or the remote processing module 270 can processthe audio data from the microphone (or audio data in another stream suchas, e.g., a video stream being watched by the user) to identify contentof the speech by applying various speech recognition algorithms, suchas, e.g., hidden Markov models, dynamic time warping (DTW)-based speechrecognitions, neural networks, deep learning algorithms such as deepfeedforward and recurrent neural networks, end-to-end automatic speechrecognitions, machine learning algorithms (described with reference toFIG. 7), or other algorithms that uses acoustic modeling or languagemodeling, etc.

The local data & processing module 260 or the remote processing module270 can also apply voice recognition algorithms which can identify theidentity of the speaker, such as whether the speaker is the user 210 ofthe wearable system 600 or another person with whom the user isconversing. Some example voice recognition algorithms can includefrequency estimation, hidden Markov models, Gaussian mixture models,pattern matching algorithms, neural networks, matrix representation,Vector Quantization, speaker diarisation, decision trees, and dynamictime warping (DTW) technique. Voice recognition techniques can alsoinclude anti-speaker techniques, such as cohort models, and worldmodels. Spectral features may be used in representing speakercharacteristics. The local data & processing module or the remote dataprocessing module 270 can use various machine learning algorithmsdescribed with reference to FIG. 7 to perform the voice recognition.

An implementation of a wearable system can use these user controls orinputs via a UI. UI elements (e.g., controls, popup windows, bubbles,data entry fields, etc.) can be used, for example, to dismiss a displayof information, e.g., graphics or semantic information of an object.

With regard to the camera systems, the example wearable system 600 shownin FIG. 6A can include three pairs of cameras: a relative wide FOV orpassive SLAM pair of cameras arranged to the sides of the user's face, adifferent pair of cameras oriented in front of the user to handle thestereo imaging process 640 and also to capture hand gestures andtotem/object tracking in front of the user's face. The FOV cameras andthe pair of cameras for the stereo process 640 may be a part of theoutward-facing imaging system 464 (shown in FIG. 4). The wearable system600 can include eye tracking cameras (which may be a part of aninward-facing imaging system 462 shown in FIG. 4) oriented toward theeyes of the user in order to triangulate eye vectors and otherinformation. The wearable system 600 may also comprise one or moretextured light projectors (such as infrared (IR) projectors) to injecttexture into a scene.

The wearable system 600 can comprise an avatar processing and renderingsystem 690. The avatar processing and rendering system 690 can beconfigured to generate, update, animate, and render an avatar based oncontextual information. Some or all of the avatar processing andrendering system 690 can be implemented as part of the local processingand data module 260 or the remote processing module 262, 264 alone or incombination. In various embodiments, multiple avatar processing andrendering systems 690 (e.g., as implemented on different wearabledevices) can be used for rendering the virtual avatar 670. For example,a first user's wearable device may be used to determine the first user'sintent, while a second user's wearable device can determine an avatar'scharacteristics and render the avatar of the first user based on theintent received from the first user's wearable device. The first user'swearable device and the second user's wearable device (or other suchwearable devices) can communicate via a network, for example, as will bedescribed with reference to FIGS. 9A and 9B.

FIG. 6B illustrates an example avatar processing and rendering system690. The example avatar processing and rendering system 690 can comprisea 3D model processing system 680, a contextual information analysissystem 688, an avatar autoscaler 692, an intent mapping system 694, ananatomy adjustment system 698, or a stimuli response system 696, aloneor in combination. The system 690 is intended to illustratefunctionalities for avatar processing and rendering and is not intendedto be limiting. For example, in certain implementations, one or more ofthese systems may be part of another system. For example, portions ofthe contextual information analysis system 688 may be part of the avatarautoscaler 692, intent mapping system 694, stimuli response system 696,or anatomy adjustment system 698, individually or in combination.

The contextual information analysis system 688 can be configured todetermine environment and object information based on one or more devicesensors described with reference to FIGS. 2 and 3. For example, thecontextual information analysis system 688 can analyze environments andobjects (including physical or virtual objects) of a user's environmentor an environment in which the user's avatar is rendered, using images(e.g., photogrammetric scans) acquired by the outward-facing imagingsystem 464 of the user or the viewer of the user's avatar. Thecontextual information analysis system 688 can analyze such images aloneor in combination with data acquired from location data or world maps(e.g., maps 620, 710, 910) to determine the location and layout ofobjects in the environments. The contextual information analysis system688 can also access biological features of the user or human in generalfor animating the virtual avatar 670 realistically. For example, thecontextual information analysis system 688 can generate a discomfortcurve which can be applied to the avatar such that a portion of theuser's avatar's body (e.g., the head) is not at an uncomfortable (orunrealistic) position with respect to the other portions of the user'sbody (e.g., the avatar's head is not turned 270 degrees). In certainimplementations, one or more object recognizers 708 (shown in FIG. 7)may be implemented as part of the contextual information analysis system688.

The avatar autoscaler 692, the intent mapping system 694, and thestimuli response system 696, and anatomy adjustment system 698 can beconfigured to determine the avatar's characteristics based on contextualinformation. Some example characteristics of the avatar can include thesize, appearance, position, orientation, movement, pose, expression,etc. The avatar autoscaler 692 can be configured to automatically scalethe avatar such that the user does not have to look at the avatar at anuncomfortable pose. For example, the avatar autoscaler 692 can increaseor decrease the size of the avatar to bring the avatar to the user's eyelevel such that the user does not need to look down at the avatar orlook up at the avatar respectively. The intent mapping system 694 candetermine an intent of a user's interaction and map the intent to anavatar (rather than the exact user interaction) based on the environmentthat the avatar is rendered in. For example, an intent of a first usermay be to communicate with a second user in a telepresence session (see,e.g., FIG. 9B). Typically, two people face each other whencommunicating. The intent mapping system 694 of the first user'swearable system can determine that such a face-to-face intent existsduring the telepresence session and can cause the first user's wearablesystem to render the second user's avatar to be facing the first user.If the second user were to physically turn around, instead of renderingthe second user's avatar in a turned position (which would cause theback of the second user's avatar to be rendered to the first user), thefirst user's intent mapping system 694 can continue to render the secondavatar's face to the first user, which is the inferred intent of thetelepresence session (e.g., face-to-face intent in this example).

The stimuli response system 696 can identify an object of interest inthe environment and determine an avatar's response to the object ofinterest. For example, the stimuli response system 696 can identify asound source in an avatar's environment and automatically turn theavatar to look at the sound source. The stimuli response system 696 canalso determine a threshold termination condition. For example, thestimuli response system 696 can cause the avatar to go back to itsoriginal pose after the sound source disappears or after a period oftime has elapsed.

The anatomy adjustment system 698 can be configured to adjust the user'spose based on biological features. For example, the anatomy adjustmentsystem 698 can be configured to adjust relative positions between theuser's head and the user's torso or between the user's upper body andlower body based on a discomfort curve.

The 3D model processing system 680 can be configured to animate andcause the display 220 to render a virtual avatar 670. The 3D modelprocessing system 680 can include a virtual character processing system682 and a movement processing system 684. The virtual characterprocessing system 682 can be configured to generate and update a 3Dmodel of a user (for creating and animating the virtual avatar). As willfurther be described with reference to FIGS. 12 and 13, the virtualcharacter processing system 682 can utilize a Facial Solver that solvesfor an avatar's facial appearance based on an input image of a subjectperforming a pose. The virtual character processing system 682 canaccess an image of a subject or data corresponding to an image of thesubject (for example, data comprising an image that can be analyzed forstructured or unstructured point cloud data, a mesh of vertices), andthe Facial Solver can successively solve for facial subregions of theentire face, rather than solving for the entire face simultaneously. TheFacial Solver can execute in a broad strokes approach (e.g., marching infrom facial subregions corresponding to large areas of movement, such asthe jaw, and progressing to more facial subregions correspond togranular details such as the eyes) until a distance error between theinput image and each facial subregion of the facial rig is reduced orminimized.

The movement processing system 684 can be configured to animate theavatar, such as, e.g., by changing the avatar's pose, by moving theavatar around in a user's environment, or by animating the avatar'sfacial expressions, etc. As will further be described herein, thevirtual avatar can be animated using rigging techniques. In someembodiments, an avatar is represented in two parts: a surfacerepresentation (e.g., a deformable mesh) that is used to render theoutward appearance of the virtual avatar and a hierarchical set ofinterconnected joints (e.g., a core skeleton) for animating the mesh. Insome implementations, the virtual character processing system 682 can beconfigured to edit or generate surface representations, while themovement processing system 684 can be used to animate the avatar bymoving the avatar, deforming the mesh, etc.

Examples of Mapping a User's Environment

FIG. 7 is a block diagram of an example of an MR environment 700. The MRenvironment 700 may be configured to receive input (e.g., visual input702 from the user's wearable system, stationary input 704 such as roomcameras, sensory input 706 from various sensors, gestures, totems, eyetracking, user input from the user input device 466 etc.) from one ormore user wearable systems (e.g., wearable system 200 or display system220) or stationary room systems (e.g., room cameras, etc.). The wearablesystems can use various sensors (e.g., accelerometers, gyroscopes,temperature sensors, movement sensors, depth sensors, GPS sensors,inward-facing imaging system, outward-facing imaging system, etc.) todetermine the location and various other attributes of the environmentof the user. This information may further be supplemented withinformation from stationary cameras in the room that may provide imagesor various cues from a different point of view. The image data acquiredby the cameras (such as the room cameras and/or the cameras of theoutward-facing imaging system) may be reduced to a set of mappingpoints.

One or more object recognizers 708 can crawl through the received data(e.g., the collection of points) and recognize or map points, tagimages, attach semantic information to objects with the help of a mapdatabase 710. The map database 710 may comprise various points collectedover time and their corresponding objects. The various devices and themap database can be connected to each other through a network (e.g.,LAN, WAN, etc.) to access the cloud.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects in anenvironment. For example, the object recognizers can recognize faces,persons, windows, walls, user input devices, televisions, documents(e.g., travel tickets, driver's license, passport as described in thesecurity examples herein), other objects in the user's environment, etc.One or more object recognizers may be specialized for object withcertain characteristics. For example, the object recognizer 708 a may beused to recognizer faces, while another object recognizer may be usedrecognize documents.

The object recognitions may be performed using a variety of computervision techniques. For example, the wearable system can analyze theimages acquired by the outward-facing imaging system 464 (shown in FIG.4) to perform scene reconstruction, event detection, video tracking,object recognition (e.g., persons or documents), object pose estimation,facial recognition (e.g., from a person in the environment or an imageon a document), learning, indexing, motion estimation, or image analysis(e.g., identifying indicia within documents such as photos, signatures,identification information, travel information, etc.), and so forth. Oneor more computer vision algorithms may be used to perform these tasks.Non-limiting examples of computer vision algorithms include:Scale-invariant feature transform (SIFT), speeded up robust features(SURF), oriented FAST and rotated BRIEF (ORB), binary robust invariantscalable keypoints (BRISK), fast retina keypoint (FREAK), Viola-Jonesalgorithm, Eigenfaces approach, Lucas-Kanade algorithm, Horn-Schunkalgorithm, Mean-shift algorithm, visual simultaneous location andmapping (vSLAM) techniques, a sequential Bayesian estimator (e.g.,Kalman filter, extended Kalman filter, etc.), bundle adjustment,Adaptive thresholding (and other thresholding techniques), IterativeClosest Point (ICP), Semi Global Matching (SGM), Semi Global BlockMatching (SGBM), Feature Point Histograms, various machine learningalgorithms (such as e.g., support vector machine, k-nearest neighborsalgorithm, Naive Bayes, neural network (including convolutional or deepneural networks), or other supervised/unsupervised models, etc.), and soforth.

The object recognitions can additionally or alternatively be performedby a variety of machine learning algorithms. Once trained, the machinelearning algorithm can be stored by the HMD. Some examples of machinelearning algorithms can include supervised or non-supervised machinelearning algorithms, including regression algorithms (such as, forexample, Ordinary Least Squares Regression), instance-based algorithms(such as, for example, Learning Vector Quantization), decision treealgorithms (such as, for example, classification and regression trees),Bayesian algorithms (such as, for example, Naive Bayes), clusteringalgorithms (such as, for example, k-means clustering), association rulelearning algorithms (such as, for example, a-priori algorithms),artificial neural network algorithms (such as, for example, Perceptron),deep learning algorithms (such as, for example, Deep Boltzmann Machine,or deep neural network), dimensionality reduction algorithms (such as,for example, Principal Component Analysis), ensemble algorithms (suchas, for example, Stacked Generalization), and/or other machine learningalgorithms. In some embodiments, individual models can be customized forindividual data sets. For example, the wearable device can generate orstore a base model. The base model may be used as a starting point togenerate additional models specific to a data type (e.g., a particularuser in the telepresence session), a data set (e.g., a set of additionalimages obtained of the user in the telepresence session), conditionalsituations, or other variations. In some embodiments, the wearable HMDcan be configured to utilize a plurality of techniques to generatemodels for analysis of the aggregated data. Other techniques may includeusing pre-defined thresholds or data values.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects andsupplement objects with semantic information to give life to theobjects. For example, if the object recognizer recognizes a set ofpoints to be a door, the system may attach some semantic information(e.g., the door has a hinge and has a 90 degree movement about thehinge). If the object recognizer recognizes a set of points to be amirror, the system may attach semantic information that the mirror has areflective surface that can reflect images of objects in the room. Thesemantic information can include affordances of the objects as describedherein. For example, the semantic information may include a normal ofthe object. The system can assign a vector whose direction indicates thenormal of the object. Over time the map database grows as the system(which may reside locally or may be accessible through a wirelessnetwork) accumulates more data from the world. Once the objects arerecognized, the information may be transmitted to one or more wearablesystems. For example, the MR environment 700 may include informationabout a scene happening in California. The environment 700 may betransmitted to one or more users in New York. Based on data receivedfrom an FOV camera and other inputs, the object recognizers and othersoftware components can map the points collected from the variousimages, recognize objects etc., such that the scene may be accurately“passed over” to a second user, who may be in a different part of theworld. The environment 700 may also use a topological map forlocalization purposes.

FIG. 8 is a process flow diagram of an example of a method 800 ofrendering virtual content in relation to recognized objects. The method800 describes how a virtual scene may be presented to a user of thewearable system. The user may be geographically remote from the scene.For example, the user may be in New York, but may want to view a scenethat is presently going on in California, or may want to go on a walkwith a friend who resides in California.

At block 810, the wearable system may receive input from the user andother users regarding the environment of the user. This may be achievedthrough various input devices, and knowledge already possessed in themap database. The user's FOV camera, sensors, GPS, eye tracking, etc.,convey information to the system at block 810. The system may determinesparse points based on this information at block 820. The sparse pointsmay be used in determining pose data (e.g., head pose, eye pose, bodypose, or hand gestures) that can be used in displaying and understandingthe orientation and position of various objects in the user'ssurroundings. The object recognizers 708 a-708 n may crawl through thesecollected points and recognize one or more objects using a map databaseat block 830. This information may then be conveyed to the user'sindividual wearable system at block 840, and the desired virtual scenemay be accordingly displayed to the user at block 850. For example, thedesired virtual scene (e.g., user in CA) may be displayed at theappropriate orientation, position, etc., in relation to the variousobjects and other surroundings of the user in New York.

Example Communications Among Multiple Wearable Systems

FIG. 9A schematically illustrates an overall system view depictingmultiple user devices interacting with each other. The computingenvironment 900 includes user devices 930 a, 930 b, 930 c. The userdevices 930 a, 930 b, and 930 c can communicate with each other througha network 990. The user devices 930 a-930 c can each include a networkinterface to communicate via the network 990 with a remote computingsystem 920 (which may also include a network interface 971). The network990 may be a LAN, WAN, peer-to-peer network, radio, Bluetooth, or anyother network. The computing environment 900 can also include one ormore remote computing systems 920. The remote computing system 920 mayinclude server computer systems that are clustered and located atdifferent geographic locations. The user devices 930 a, 930 b, and 930 cmay communicate with the remote computing system 920 via the network990.

The remote computing system 920 may include a remote data repository 980which can maintain information about a specific user's physical and/orvirtual worlds. Data storage 980 can store information related to users,users' environment (e.g., world maps of the user's environment), orconfigurations of avatars of the users. The remote data repository maybe an embodiment of the remote data repository 280 shown in FIG. 2. Theremote computing system 920 may also include a remote processing module970. The remote processing module 970 may be an embodiment of the remoteprocessing module 270 shown in FIG. 2. The remote processing module 970may include one or more processors which can communicate with the userdevices (930 a, 930 b, 930 c) and the remote data repository 980. Theprocessors can process information obtained from user devices and othersources. In some implementations, at least a portion of the processingor storage can be provided by the local processing and data module 260(as shown in FIG. 2). The remote computing system 920 may enable a givenuser to share information about the specific user's own physical and/orvirtual worlds with another user.

The user device may be a wearable device (such as an HMD or an ARD), acomputer, a mobile device, or any other devices alone or in combination.For example, the user devices 930 b and 930 c may be an embodiment ofthe wearable system 200 shown in FIG. 2 (or the wearable system 400shown in FIG. 4) which can be configured to present AR/VR/MR content.

One or more of the user devices can be used with the user input device466 shown in FIG. 4. A user device can obtain information about the userand the user's environment (e.g., using the outward-facing imagingsystem 464 shown in FIG. 4). The user device and/or remote computingsystem 1220 can construct, update, and build a collection of images,points and other information using the information obtained from theuser devices. For example, the user device may process raw informationacquired and send the processed information to the remote computingsystem 1220 for further processing. The user device may also send theraw information to the remote computing system 1220 for processing. Theuser device may receive the processed information from the remotecomputing system 1220 and provide final processing before projecting tothe user. The user device may also process the information obtained andpass the processed information to other user devices. The user devicemay communicate with the remote data repository 1280 while processingacquired information. Multiple user devices and/or multiple servercomputer systems may participate in the construction and/or processingof acquired images.

The information on the physical worlds may be developed over time andmay be based on the information collected by different user devices.Models of virtual worlds may also be developed over time and be based onthe inputs of different users. Such information and models can sometimesbe referred to herein as a world map or a world model. As described withreference to FIGS. 6 and 7, information acquired by the user devices maybe used to construct a world map 910. The world map 910 may include atleast a portion of the map 620 described in FIG. 6A. Various objectrecognizers (e.g. 708 a, 708 b, 708 c . . . 708 n) may be used torecognize objects and tag images, as well as to attach semanticinformation to the objects. These object recognizers are also describedin FIG. 7.

The remote data repository 980 can be used to store data and tofacilitate the construction of the world map 910. The user device canconstantly update information about the user's environment and receiveinformation about the world map 910. The world map 910 may be created bythe user or by someone else. As discussed herein, user devices (e.g. 930a, 930 b, 930 c) and remote computing system 920, alone or incombination, may construct and/or update the world map 910. For example,a user device may be in communication with the remote processing module970 and the remote data repository 980. The user device may acquireand/or process information about the user and the user's environment.The remote processing module 970 may be in communication with the remotedata repository 980 and user devices (e.g. 930 a, 930 b, 930 c) toprocess information about the user and the user's environment. Theremote computing system 920 can modify the information acquired by theuser devices (e.g. 930 a, 930 b, 930 c), such as, e.g. selectivelycropping a user's image, modifying the user's background, adding virtualobjects to the user's environment, annotating a user's speech withauxiliary information, etc. The remote computing system 920 can send theprocessed information to the same and/or different user devices.

Examples of a Telepresence Session

FIG. 9B depicts an example where two users of respective wearablesystems are conducting a telepresence session. Two users (named Alice912 and Bob 914 in this example) are shown in this figure. The two usersare wearing their respective wearable devices 902 and 904 which caninclude an HMD described with reference to FIG. 2 (e.g., the displaydevice 220 of the system 200) for representing a virtual avatar of theother user in the telepresence session. The two users can conduct atelepresence session using the wearable device. Note that the verticalline in FIG. 9B separating the two users is intended to illustrate thatAlice 912 and Bob 914 may (but need not) be in two different locationswhile they communicate via telepresence (e.g., Alice may be inside heroffice in Atlanta while Bob is outdoors in Boston).

As described with reference to FIG. 9A, the wearable devices 902 and 904may be in communication with each other or with other user devices andcomputer systems. For example, Alice's wearable device 902 may be incommunication with Bob's wearable device 904, e.g., via the network 990(shown in FIG. 9A). The wearable devices 902 and 904 can track theusers' environments and movements in the environments (e.g., via therespective outward-facing imaging system 464, or one or more locationsensors) and speech (e.g., via the respective audio sensor 232). Thewearable devices 902 and 904 can also track the users' eye movements orgaze based on data acquired by the inward-facing imaging system 462. Insome situations, the wearable device can also capture or track a user'sfacial expressions or other body movements (e.g., arm or leg movements)where a user is near a reflective surface and the outward-facing imagingsystem 464 can obtain reflected images of the user to observe the user'sfacial expressions or other body movements.

A wearable device can use information acquired of a first user and theenvironment to animate a virtual avatar that will be rendered by asecond user's wearable device to create a tangible sense of presence ofthe first user in the second user's environment. For example, thewearable devices 902 and 904, the remote computing system 920, alone orin combination, may process Alice's images or movements for presentationby Bob's wearable device 904 or may process Bob's images or movementsfor presentation by Alice's wearable device 902. As further describedherein, the avatars can be rendered based on contextual information suchas, e.g., a user's intent, an environment of the user or an environmentin which the avatar is rendered, or other biological features of ahuman.

Although the examples only refer to two users, the techniques describedherein should not be limited to two users. Multiple users (e.g., two,three, four, five, six, or more) using wearables (or other telepresencedevices) may participate in a telepresence session. A particular user'swearable device can present to that particular user the avatars of theother users during the telepresence session. Further, while the examplesin this figure show users as standing in an environment, the users arenot required to stand. Any of the users may stand, sit, kneel, lie down,walk or run, or be in any position or movement during a telepresencesession. The user may also be in a physical environment other thandescribed in examples herein. The users may be in separate environmentsor may be in the same environment while conducting the telepresencesession. Not all users are required to wear their respective HMDs in thetelepresence session. For example, Alice 912 may use other imageacquisition and display devices such as a webcam and computer screenwhile Bob 914 wears the wearable device 904.

Examples of a Virtual Avatar

FIG. 10 illustrates an example of an avatar as perceived by a user of awearable system. The example avatar 1000 shown in FIG. 10 can be anavatar of Alice 912 (shown in FIG. 9B) standing behind a physical plantin a room. An avatar can include various characteristics, such as forexample, size, appearance (e.g., skin color, complexion, hair style,clothes, facial features, such as wrinkles, moles, blemishes, pimples,dimples, etc.), position, orientation, movement, pose, expression, etc.These characteristics may be based on the user associated with theavatar (e.g., the avatar 1000 of Alice may have some or allcharacteristics of the actual person Alice 912). As further describedherein, the avatar 1000 can be animated based on contextual information,which can include adjustments to one or more of the characteristics ofthe avatar 1000. Although generally described herein as representing thephysical appearance of the person (e.g., Alice), this is forillustration and not limitation. Alice's avatar could represent theappearance of another real or fictional human being besides Alice, apersonified object, a creature, or any other real or fictitiousrepresentation. Further, the plant in FIG. 10 need not be physical, butcould be a virtual representation of a plant that is presented to theuser by the wearable system. Also, additional or different virtualcontent than shown in FIG. 10 could be presented to the user.

Examples of Rigging Systems for Virtual Characters

An animated virtual character, such as a human avatar, can be wholly orpartially represented in computer graphics as a polygon mesh. A polygonmesh, or simply “mesh” for short, is a collection of points in a modeledthree-dimensional space. The mesh can form a polyhedral object whosesurfaces define the body or shape of the virtual character (or a portionthereof). While meshes can include any number of points (withinpractical limits which may be imposed by available computing power),finer meshes with more points are generally able to portray morerealistic virtual characters with finer details that may closelyapproximate real life people, animals, objects, etc. FIG. 10 shows anexample of a mesh 1010 around an eye of the avatar 1000.

Each point in the mesh can be defined by a coordinate in the modeledthree-dimensional space. The modeled three-dimensional space can be, forexample, a Cartesian space addressed by (x, y, z) coordinates. Thepoints in the mesh are the vertices of the polygons which make up thepolyhedral object. Each polygon represents a surface, or face, of thepolyhedral object and is defined by an ordered set of vertices, with thesides of each polygon being straight line edges connecting the orderedset of vertices. In some cases, the polygon vertices in a mesh maydiffer from geometric polygons in that they are not necessarily coplanarin 3D graphics. In addition, the vertices of a polygon in a mesh may becollinear, in which case the polygon has zero area (referred to as adegenerate polygon).

In some embodiments, a mesh is made up of three-vertex polygons (i.e.,triangles or “tris” for short) or four-vertex polygons (i.e.,quadrilaterals or “quads” for short). However, higher-order polygons canalso be used in some meshes. Meshes are typically quad-based in directcontent creation (DCC) applications (e.g., applications such as Maya(available from Autodesk, Inc.) or Houdini (available from Side EffectsSoftware Inc.) which are primarily designed for creating andmanipulating 3D computer graphics), whereas meshes are typicallytri-based in real-time applications.

To animate a virtual character, its mesh can be deformed by moving someor all of its vertices to new positions in space at various instants intime. The deformations can represent both large-scale movements (e.g.,movement of limbs) and fine movements (e.g., facial movements). Theseand other deformations can be based on real-world models (e.g.,photogrammetric scans of real humans performing body movements,articulations, facial contortions, expressions, etc.), art-directeddevelopment (which may be based on real-world sampling), combinations ofthe same, or other techniques. In the early days of computer graphics,mesh deformations could be accomplished manually by independentlysetting new positions for the vertices, but given the size andcomplexity of modern meshes it is typically desirable to producedeformations using automated systems and processes. The control systems,processes, and techniques for producing these deformations are referredto as rigging, or simply “the rig.” The example avatar processing andrendering system 690 of FIG. 6B includes a 3D model processing system680 which can implement rigging, such as, e.g., by converting fromunstructured point cloud data to rig data or by converting from one rigdata to another rig data.

The rigging for a virtual character can use skeletal systems to assistwith mesh deformations. A skeletal system includes a collection ofjoints which correspond to points of articulation for the mesh. In thecontext of rigging, joints are sometimes also referred to as “bones”despite the difference between these terms when used in the anatomicalsense. Joints in a skeletal system can move, or otherwise change, withrespect to one another according to transforms which can be applied tothe joints. The transforms can include translations or rotations inspace, as well as other operations. The joints can be assignedhierarchical relationships (e.g., parent-child relationships) withrespect to one another. These hierarchical relationships can allow onejoint to inherit transforms or other characteristics from another joint.For example, a child joint in a skeletal system can inherit a transformassigned to its parent joint so as to cause the child joint to movetogether with the parent joint.

A skeletal system for a virtual character can be defined with joints atappropriate positions, and with appropriate local axes of rotation,degrees of freedom, etc., to allow for a desired set of meshdeformations to be carried out. Once a skeletal system has been definedfor a virtual character, each joint can be assigned, in a process called“skinning,” an amount of influence over the various vertices in themesh. This can be done by assigning a weight value to each vertex foreach joint in the skeletal system. When a transform is applied to anygiven joint, the vertices under its influence can be moved, or otherwisealtered, automatically based on that joint transform by amounts whichcan be dependent upon their respective weight values.

A rig can include multiple skeletal systems. One type of skeletal systemis a core skeleton (also referred to as a low-order skeleton) which canbe used to control large-scale movements of the virtual character. Inthe case of a human avatar, for example, the core skeleton mightresemble the anatomical skeleton of a human. Although the core skeletonfor rigging purposes may not map exactly to an anatomically-correctskeleton, it may have a subset of joints in analogous locations withanalogous orientations and movement properties.

As briefly mentioned above, a skeletal system of joints can behierarchical with, for example, parent-child relationships among joints.When a transform (e.g., a change in position and/or orientation) isapplied to a particular joint in the skeletal system, the same transformcan be applied to all other lower-level joints within the samehierarchy. In the case of a rig for a human avatar, for example, thecore skeleton may include separate joints for the avatar's shoulder,elbow, and wrist. Among these, the shoulder joint may be assigned to thehighest level in the hierarchy, while the elbow joint can be assigned asa child of the shoulder joint, and the wrist joint can be assigned as achild of the elbow joint. Accordingly, when a particular translationand/or rotation transform is applied to the shoulder joint, the sametransform can also be applied to the elbow joint and the wrist jointsuch that they are translated and/or rotated in the same way as theshoulder.

Despite the connotations of its name, a skeletal system in a rig neednot necessarily represent an anatomical skeleton. In rigging, skeletalsystems can represent a wide variety of hierarchies used to controldeformations of the mesh. For example, hair can be represented as aseries of joints in a hierarchical chain; skin motions due to anavatar's facial contortions (which may represent expressions such assmiling, frowning, laughing, speaking, blinking, etc.) can berepresented by a series of facial joints controlled by a facial rig;muscle deformation can be modeled by joints; and motion of clothing canbe represented by a grid of joints.

The rig for a virtual character can include multiple skeletal systems,some of which may drive the movement of others. A lower-order skeletalsystem is one which drives one or more higher-order skeletal systems.Conversely, higher-order skeletal systems are ones which are driven orcontrolled by a lower-order skeletal system. For example, whereas themovements of the core skeleton of a character might be controlledmanually by an animator, the core skeleton can in turn drive or controlthe movements of a higher-order skeletal system. For example,higher-order helper joints—which may not have anatomical analogs in aphysical skeleton—can be provided to improve the mesh deformations whichresult from movements of the core skeleton. The transforms applied tothese and other joints in higher-order skeletal systems may be derivedalgorithmically from the transforms applied to the lower-order skeleton.Higher-order skeletons can represent, for example, muscles, skin, fat,clothing, hair, or any other skeletal system which does not requiredirect animation control.

As already discussed, transforms can be applied to joints in skeletalsystems in order to carry out mesh deformations. In the context ofrigging, transforms include functions which accept one or more givenpoints in 3D space and produce an output of one or more new 3D points.For example, a transform can accept one or more 3D points which define ajoint and can output one or more new 3D points which specify thetransformed joint. Joint transforms can include, for example, atranslation component, a rotation component, and a scale component.

A translation is a transform which moves a set of one or more specifiedpoints in the modeled 3D space by a specified amount with no change inthe orientation or size of the set of points. A rotation is a transformwhich rotates a set of one or more specified points in the modeled 3Dspace about a specified axis by a specified amount (e.g., rotate everypoint in the mesh 45 degrees about the z-axis). An affine transform (or6 degree of freedom (DOF) transform) is one which only includestranslation(s) and rotation(s). Application of an affine transform canbe thought of as moving a set of one or more points in space withoutchanging its size, though the orientation can change.

Meanwhile, a scale transform is one which modifies one or more specifiedpoints in the modeled 3D space by scaling their respective coordinatesby a specified value. This changes the size and/or shape of thetransformed set of points. A uniform scale transform scales eachcoordinate by the same amount, whereas a non-uniform scale transform canscale the (x, y, z) coordinates of the specified points independently. Anon-uniform scale transform can be used, for example, to providesquashing and stretching effects, such as those which may result frommuscular action. Yet another type of transform is a shear transform. Ashear transform is one which modifies a set of one or more specifiedpoints in the modeled 3D space by translating a coordinate of the pointsby different amounts based on the distance of that coordinate from anaxis.

When a transform is applied to a joint to cause it to move, the verticesunder the influence of that joint are also moved. This results indeformations of the mesh. As discussed above, the process of assigningweights to quantify the influence each joint has over each vertex iscalled skinning (or sometimes “weight painting” or “skin weighting”).The weights are typically values between 0 (meaning no influence) and 1(meaning complete influence). Some vertices in the mesh may beinfluenced only by a single joint. In that case those vertices areassigned weight values of 1 for that joint, and their positions arechanged based on transforms assigned to that specific joint but noothers. Other vertices in the mesh may be influenced by multiple joints.In that case, separate weights are assigned to those vertices for all ofthe influencing joints, with the sum of the weights for each vertexequaling 1. The positions of these vertices are changed based ontransforms assigned to all of their influencing joints.

Making weight assignments for all of the vertices in a mesh can beextremely labor intensive, especially as the number of joints increases.Balancing the weights to achieve desired mesh deformations in responseto transforms applied to the joints can be quite difficult for evenhighly trained artists. In the case of real-time applications, the taskcan be complicated further by the fact that many real-time systems alsoenforce limits on the number of joints (generally 8 or fewer) which canbe weighted to a specific vertex. Such limits are typically imposed forthe sake of efficiency in the graphics processing unit (GPU).

The term skinning also refers to the process of actually deforming themesh, using the assigned weights, based on transforms applied to thejoints in a skeletal system. For example, a series of core skeletonjoint transforms may be specified by an animator to produce a desiredcharacter movement (e.g., a running movement or a dance step). Whentransforms are applied to one or more of the joints, new positions arecalculated for the vertices under the influence of the transformedjoints. The new position for any given vertex is typically computed as aweighted average of all the joint transforms which influence thatparticular vertex. There are many algorithms used for computing thisweighted average, but the most common, and the one used in mostreal-time applications due to its simplicity and ease of control, islinear blend skinning (LBS). In linear blend skinning, a new positionfor each vertex is calculated using each joint transform for which thatvertex has a non-zero weight. Then, the new vertex coordinates resultingfrom each of these joint transforms are averaged in proportion to therespective weights assigned to that vertex for each of the joints. Thereare well known limitations to LBS in practice, and much of the work inmaking high-quality rigs is devoted to finding and overcoming theselimitations. Many helper joint systems are designed specifically forthis purpose.

In addition to skeletal systems, “blendshapes” can also be used inrigging to produce mesh deformations. A blendshape (sometimes alsocalled a “morph target” or just a “shape”) is a deformation applied to aset of vertices in the mesh where each vertex in the set is moved aspecified amount in a specified direction based upon a weight. Eachvertex in the set may have its own custom motion for a specificblendshape, and moving the vertices in the set simultaneously willgenerate the desired shape. The custom motion for each vertex in ablendshape can be specified by a “delta,” which is a vector representingthe amount and direction of XYZ motion applied to that vertex.Blendshapes can be used to produce, for example, facial deformations tomove the eyes, lips, brows, nose, dimples, etc., just to name a fewpossibilities.

Blendshapes are useful for deforming the mesh in an art-directable way.They offer a great deal of control, as the exact shape can be sculptedor captured from a scan of a model. But the benefits of blendshapes comeat the cost of having to store the deltas for all the vertices in theblendshape. For animated characters with fine meshes and manyblendshapes, the amount of delta data can be significant.

Each blendshape can be applied to a specified degree by using blendshapeweights. These weights typically range from 0 (where the blendshape isnot applied at all) to 1 (where the blendshape is fully active). Forexample, a blendshape to move a character's eyes can be applied with asmall weight to move the eyes a small amount, or it can be applied witha large weight to create a larger eye movement.

The rig may apply multiple blendshapes in combinations with one anotherto achieve a desired complex deformation. For example, to produce asmile, the rig may apply blendshapes for lip corner pull, raising theupper lip, and lowering the lower lip, as well as moving the eyes,brows, nose, and dimples. The desired shape from combining two or moreblendshapes is known as a combination shape (or simply a “combo”).

One problem that can result from applying two blendshapes in combinationis that the blendshapes may operate on some of the same vertices. Whenboth blendshapes are active, the result is called a double transform or“going off-model.” The solution to this is typically a correctiveblendshape. A corrective blendshape is a special blendshape whichrepresents a desired deformation with respect to a currently applieddeformation rather than representing a desired deformation with respectto the neutral. Corrective blendshapes (or just “correctives”) can beapplied based upon the weights of the blendshapes they are correcting.For example, the weight for the corrective blendshape can be madeproportionate to the weights of the underlying blendshapes which triggerapplication of the corrective blendshape.

Corrective blendshapes can also be used to correct skinning anomalies orto improve the quality of a deformation. For example, a joint mayrepresent the motion of a specific muscle, but as a single transform itcannot represent all the non-linear behaviors of the skin, fat, andmuscle. Applying a corrective, or a series of correctives, as the muscleactivates can result in more pleasing and convincing deformations.

Rigs are built in layers, with lower, simpler layers often drivinghigher-order layers. This applies to both skeletal systems andblendshape deformations. For example, as already mentioned, the riggingfor an animated virtual character may include higher-order skeletalsystems which are controlled by lower-order skeletal systems. There aremany ways to control a higher-order skeleton or a blendshape based upona lower-order skeleton, including constraints, logic systems, andpose-based deformation.

A constraint is typically a system where a particular object or jointtransform controls one or more components of a transform applied toanother joint or object. There are many different types of constraints.For example, aim constraints change the rotation of the target transformto point in specific directions or at specific objects. Parentconstraints act as virtual parent-child relationships between pairs oftransforms. Position constraints constrain a transform to specificpoints or a specific object. Orientation constraints constrain atransform to a specific rotation of an object.

Logic systems are systems of mathematical equations which produce someoutputs given a set of inputs. These are specified, not learned. Forexample, a blendshape value might be defined as the product of two otherblendshapes (this is an example of a corrective shape known as acombination or combo shape).

Pose-based deformations can also be used to control higher-orderskeletal systems or blendshapes. The pose of a skeletal system isdefined by the collection of transforms (e.g., rotation(s) andtranslation(s)) for all the joints in that skeletal system. Poses canalso be defined for subsets of the joints in a skeletal system. Forexample, an arm pose could be defined by the transforms applied to theshoulder, elbow, and wrist joints. A pose space deformer (PSD) is asystem used to determine a deformation output for a particular posebased on one or more “distances” between that pose and a defined pose.These distances can be metrics which characterize how different one ofthe poses is from the other. A PSD can include a pose interpolation nodewhich, for example, accepts a set of joint rotations (defining a pose)as input parameters and in turn outputs normalized per-pose weights todrive a deformer, such as a blendshape. The pose interpolation node canbe implemented in a variety of ways, including with radial basisfunctions (RBFs). RBFs can perform a machine-learned mathematicalapproximation of a function. RBFs can be trained using a set of inputsand their associated expected outputs. The training data could be, forexample, multiple sets of joint transforms (which define particularposes) and the corresponding blendshapes to be applied in response tothose poses. Once the function is learned, new inputs (e.g., poses) canbe given and their expected outputs can be computed efficiently. RBFsare a subtype of artificial neural networks. RBFs can be used to drivehigher-level components of a rig based upon the state of lower-levelcomponents. For example, the pose of a core skeleton can drive helperjoints and correctives at higher levels.

These control systems can be chained together to perform complexbehaviors. As an example, an eye rig could contain two “look around”values for horizontal and vertical rotation. These values can be passedthrough some logic to determine the exact rotation of an eye jointtransform, which might in turn be used as an input to an RBF whichcontrols blendshapes that change the shape of the eyelid to match theposition of the eye. The activation values of these shapes might be usedto drive other components of a facial expression using additional logic,and so on.

The goal of rigging systems is typically to provide a mechanism toproduce pleasing, high-fidelity deformations based on simple,human-understandable control systems. In the case of real-timeapplications, the goal is typically to provide rigging systems which aresimple enough to run in real-time on, for example, a VR/AR/MR system200, while making as few compromises to the final quality as possible.In some embodiments, the 3D model processing system 680 executes arigging system to animate an avatar in a mixed reality environment 100in real-time to be interactive (with users of the VR/AR/MR system) andto provide appropriate, contextual avatar behavior (e.g., intent-basedbehavior) in the user's environment.

Facial Action Coding System (FACS)

The Facial Action Coding System (FACS) is a system for measuring facialexpressions or determining an emotional state based on the appearance ofa person's face. FACS is a convenient tool for describing all observablefacial movement because it breaks down facial expressions intoindividual components of muscle movement. Each of the components ofmuscle movement of FACS is associated with a numerical representation(referred to as an Action Unit (AU)). FACS utilizes approximately 46basic AUs (approximately 100 AUs in total). Examples of various AUs areprovided in Table 1, below.

TABLE 1 Example FACS AUs 0 - Neutral face 1 - Inner Brow Raiser 2 -Outer Brow Raiser 4 - Brow Lowerer 5 - Upper Lid Raiser 6 - Cheek Raiser7 - Lid Tightener 8 - Lips Toward Each Other 9 - Nose Wrinkler 10 -Upper Lip Raiser 11 - Nasolabial Deepener 12 - Lip Corner Puller 13 -Sharp Lip Puller 14 - Dimpler 15 - Lip Corner Depressor 16 - Lower LipDepressor 17 - Chin Raiser 18 - Lip Pucker 19 - Tongue Out 20 - Lipstretcher 21 - Neck Tightener 22 - Lip Funneler 23 - Lip Tightener 24 -Lip Pressor 25 - Lips part 26 - Jaw Drop 27 - Mouth Stretch 28 - LipSuck 29 - Jaw Thrust 30 - Jaw Sideways 31 - Jaw Clencher 32 - Lip Bite33 - Cheek Blow 34 - Cheek Puff 35 - Cheek Suck 36 - Tongue Bulge 37 -Lip Wipe 38 - Nostril Dilator 39 - Nostril Compressor 40 - Sniff 41 -Lid Droop 42 - Slit 43 - Eyes Closed 44 - Squint 45 - Blink 46 - Wink

Using FACS, nearly any anatomically possible facial expression can becoded by deconstructing the facial expression into specific AUs thatproduced the expression.

Facial expressions are often made up of combinations of AUs such thatcertain combined movements of these facial muscles (e.g., combined AUs)can represent a particular emotion. For example, a code combination ofAU6 and AU12 together might represent a sincere and involuntary smile(e.g., happiness). Further, FACS recognizes differing levels ofintensity by appending letters A-E (for minimum-maximum intensity) tothe AU number (e.g. AU 1A is the weakest trace of AU 1 and AU 1E is themaximum intensity possible for the individual person).

Although FACS is a convenient and widely-use approach to categorizingfacial movements, the avatar animation techniques described herein arenot limited to the FACS approach. In other embodiments, other public orproprietary facial coding systems can be used to characterize avatarfacial movements or to order subregions of the face for iterativeanalysis by the Facial Solver.

Example Difficulties in Creating High Fidelity Avatar

Traditionally, creation of a high fidelity digital avatar can take manyweeks or months of work by a specialized team and can utilize a largenumber of high quality digitized photographic scans of the human model.However, in many cases, there may be insufficient time to performmassive mathematical computations to achieve highly realistic animationeffects, particularly for AR/MR/VR systems where many different subjects(which may include users of the AR/MR/R system) may want to have theirfacial poses digitized and used for avatar animation. Accordingly,various approaches have been used to reduce the time and effort requiredto create a high-fidelity avatar.

Given their cohesive nature, it is a common tendency to want to solvefor all the parameters for the subject's entire face simultaneously.However, fitting a model with many parameters at each iteration whenperforming error minimization often results in overfitting. Overfittinghappens when a model's inputs are excessively complex, such as havingtoo many parameters to be fit to the data. Overfitting has poorpredictive and computational performance, and may become sensitive tominor fluctuations in the input training data. The undesirable varianceof the output from overfitting may manifest as high frequency artifactscommonly referred to as popping.

FIG. 11 illustrates an example of overfitting data. Here, graph 1100includes line 1102 representing the overfitting and line 1104representing a straight-line best fit to data (represented by the pointsin the graph 1100). As illustrated, noisy, yet roughly linear, data isfitted to a linear function and a nonlinear, polynomial function.Although the polynomial function (e.g., corresponding to the line 1102)is a nearly perfect fit to the data points, the linear function (e.g.,corresponding to line 1104) can be expected to represent the upwardtrend in the data better. In other words, if the two functions were usedto extrapolate beyond the fit data, the linear function (e.g., line1104) would make better predictions than the polynomial fit 1102.Accordingly, one challenge to fitting a facial model to a subject imageis for the system to provide improved or optimal results thatquantitatively possess a low error (e.g., an L2 (Euclidean) norm)without overfitting.

In some cases, the Facial Solver system can implement scalarminimization to reduce a computational load and avoid overfitting. Thatis, the system can reduce a number of points to be matched between animage of a person and the avatar. For example, instead of 125 points,the system may attempt to match 50 points or even 30 points, which wouldallow the system to compute all of the parameters simultaneously withoutoverfitting. However, scalar minimization generally results in a coarserresolution basis and lower fidelity. As a consequence, severalexpressions that appear different in the input images, may appear thesame when converted to the avatar rig.

Alternatively, some systems can utilize manual input. That is, ananimator hand-manipulates an output to fix problems that occur duringautomated processing. Similarly, the system could re-project atwo-dimensional (2D) set of features onto a coarser version of theavatar to make the avatar seem higher fidelity. In other words, thesystem may solve a less complex equation for geometric space, andsupplement the geometry with a second dataset in 2D space for texture.However, this still may result in a low fidelity representation of theavatar.

For other approaches, a system may use a point-to-point correspondencebetween a subject and an avatar, without the extra step of converting torig values. However, such a method will typically not produce a highfidelity output because expressions start to blur together and fidelityof translation may be reduced. Further, software tracking of points doesnot generally produce a high fidelity output without requiring anincrease in execution time and complexity, or a reduction of power onthe device. For example, every time the system executes a point-to-pointcorrespondence, at least the primary facial expressions would have to beannotated (to make the method run smoother) and the system would alsohave to calibrate between the avatar face and the subject face, whichincreases complexity and/or human intervention.

Still, some approaches track 2D feature points on a user's face andinfer 3D blendshape parameter data from the 2D feature locations. Suchapproaches require a calibration from the user that can be confusing, aswell as lengthy to set up. In addition, the inferences from the 2Dfeature points result in reduced precision and fidelity, often resultingin a rendering of lower quality or lower fidelity interpretations of theinput emotional state.

Embodiments of the Facial Solver described herein address some or all ofthese challenges and difficulties.

Example Facial Solver

As described above, the Facial Action Coding System (FACS) classifiesobservable facial expressions based on the appearance of a person's faceby decomposing the facial expressions into isolated muscle contractionsor relaxations (see, e.g., Table 1). Each isolated muscle contraction orrelaxation of FACS is associated with a numerical representation,referred to as an Action Unit (AU). However, FACS fails to effectivelyrelate the underlying anatomy of the face. Accordingly, embodimentsdisclosed herein incorporate the FACS taxonomy into a methodology usedby a Facial Solver that separates facial expressions into a plurality offacial subregions using fixed boundaries. Any number of facialsubregions may be used, for example, 2, 3, 4, 5, 6, 8, 10, 11, 14, 20,25, 30, or more subregions. The Facial Solver then successively solves(e.g., by reducing or minimizing an error metric between the subject'simage data and the avatar rig parameters) for each facial subregionuntil all facial rig parameters are calculated for the entire face,thereby quickly and efficiently determining rig parameters that define aspecific facial expression (e.g., pose). By solving for each facialsubregion in turn, rather than solving for all facial subregionssimultaneously, the Facial Solver can reduce overall computation time.In addition, by solving the facial rig parameters in an ordered group(e.g., based on facial anatomy), the results of the Facial Solver aremore biologically motivated than previous avatar creation efforts.

The Facial Solver can access an image of a human subject and can convertthe image into a high fidelity digital avatar using facial mappingtechniques that successively solve for subregions of the face subregion,rather than solving for the entire face simultaneously. For example, thefacial subregions can be solved for in the order of highest to lowestimpact on fundamental facial movements (e.g., largest to smallestmovements or gross to fine movement). For each facial subregion, thedisclosed Facial Solver can perform a minimization operation thatadjusts various facial rig parameters of the particular facial subregionto reduce an error metric between the image and the facial rigparameters of the particular facial subregion. In some implementations,the order of the solution for the subregions proceeds from the jawsubregion, to the lower face subregion, to the funneler subregion, tothe lips, to the upper face, to the eyelids, to the eyes, to the neck,and then to extras for the lips, to the tongue, and to miscellaneousfine scale structures of the face.

Each of the facial subregions can correspond to a combination of one ormore body parts. It can be advantageous if the system divides a faceinto a sufficient number of facial subregions. If there is too muchsubdivision (e.g., too many facial subregions), key relationshipsbetween parts may be lost and accuracy of the avatar may decrease. Incontrast, if there is not enough subdivision (e.g., too few facialsubregions), calculation may take longer and popping from frame to framemay occur (e.g., overfitting), which can lead to lower accuracy. Anynumber of facial subregions may be used. For example, 8, 10, 11, 12, 13,14, 15, 16, or more subregions may be used. For example, the facialsubregions can include: a jaw subregion, a lower face subregion, a lipssubregion, a funneler subregion, an upper face subregion, an eyelidssubregion, an eyes subregion, a neck subregion, a lips subregion, atongue subregion, and a miscellaneous subregion (e.g., comprising templeveins, chin lines, small ear or cheek movements, etc.).

As described herein, the Facial Solver converts a first set of data to asecond set of data. In various cases, the first set of data can bedifferent from the second set of data. For example, the first set ofdata can be unstructured point cloud data representing the subjectperforming a pose, and the second set of data can be rig data used foranimating an avatar to perform the pose. However, the first set of dataneed not be limited to unstructured data. Rather, the first set of datacan be any data that is different or disparate from the second set ofdata. Disparate can refer to being different in kind or allowing littleto no comparison. For example, even when moving from first rig data tosecond rig data, the first rig data may appear unstructured to thesecond rig, because the data are not the same. Furthermore, not all rigshave the same number of vertices or the same vertex identifications(IDs), which increases a likelihood that there may be little or notopological relationship between rigs. Thus, embodiments quickly andaccurately correlate disparate data using mechanics-motivated andSolver-ordered groupings of parameters.

Considerable diagnostics, both quantitative and qualitative, have beenevaluated for embodiments of a Facial Solver, and it has been found thatthe use of a FACS taxonomy of fixed boundaries that are biologicallyordered outperforms a typical scalar minimization scheme for the entireface. Advantageously, the results of the parameter minimization are morebiologically motivated and free from local minima artifacts.Accordingly, embodiments of the disclosed technology have the capabilityof creating high quality or high fidelity avatars (or digitalrepresentations in general) for any human user. In order to accomplishthis, embodiments of the disclosed process are faster and less resourceintense while still maintaining an accurate output.

In any of the techniques, rig data can be for the face or body of anavatar, which can represent a human or another creature, or any objectthat moves or deforms. Any of the embodiments of the Facial Solverdescribed herein may be implemented and performed by the avatarprocessing and rendering system 690 described with reference to FIG. 6B,for example, by the 3D model processing system 680. Further, althoughthe Facial Solver may be described as minimizing an error metric, thisis intended to include reducing the error metric to a reasonably smallvalue, or iterating for a sufficient number of iterations, so that thefinal error metric is smaller than the initial error metric.Accordingly, in a minimization operation, a mathematically exact localor global minimum is not required to be achieved or found by the FacialSolver. Furthermore, although the Solver is generally referred to as aFacial Solver, the same or similar techniques or methods describedherein may be applied to other portions of a user's body (for example,feet, hands, arms, legs, chest, shoulders, neck, back, ears, hips, orthe like) or may be applied to one or more or deformable virtualobjects.

Example Facial Rig Details

As described herein, FACS decomposes facial expressions into isolatedmuscle contractions or relaxations of facial movement, and each isolatedmuscle contraction or relaxation is associated with a numericalrepresentation, referred to as an Action Unit (AU). Thus, the FACScoding system utilizes AUs to numerically categorize facial musclecontractions or relaxations. Leveraging machine-learning principles,embodiments of the system are able to transfer the facial performancefrom an arbitrary temporal point cloud to a representational avatarfacial rig.

FIG. 12 schematically illustrates an example of a facial rig 1200 thatcan be used to animate an avatar in an AR/VR/MR system. The facial rig1200 can be thought of as a digital puppet in which points ofarticulation are parameterized into a plurality of facial rig parameters1204, 1208. The facial rig 1200 electronically adjusts values for thepoints of articulation to generate a desired facial expression oremotion of the avatar. The facial rig parameters 1204, 1208 can bedirectly mapped to AUs of the FACS characterization of the human face.For example, arrows 1204 correspond to an AU parameter and lines 1208indicate the directionality of the AUs. The facial rig parameters 1204,1208 can be adjusted to control the deformation of the digital puppet1200.

The controls (e.g., facial rig parameters 1204, 1208) can be driven inreal time by the facial rig and can be parameterized to a normalizedvalue, such as between −1 and 1 or between 0 and 1. By normalizing thecontrols, the values can be used from rig to rig. For example, if a FACSsession is performed for a human model, the system can advantageouslyre-use that data on another person.

As described above, combinations of the AUs can aggregate to formrepresentations of emotional states of the human face (sometimesreferred to as AU variants). Further, an intensity scale (e.g., between0% and 100%) may be included for each AU of the AU variant. For example,the expression “Happy” can correspond to AUs: 12 (lip corner puller)(100%), 25 (lips apart) (100%), and 6 (Cheek Raiser) (51%). In somecases, if the intensity is not specified, the intensity will be set at adefault value (e.g., 100%). For example, in some cases, “Happy” can berepresented as the AU variant [12, 25, 6 (51%)].

Various other expressions can also be represented, including, but notlimited to, sad, fearful, angry, surprised, or disgusted. For example,the expression “Sad” can correspond to AUs: 4 (Brow Lowerer) (100%), 15(Lip Corner Depressor) (100%), 1 (Inner Brow Raiser) (60%), 6 (CheekRaiser) (50%), 11 (Nasolabial Deepener) (26%), and 17 (Chin Raiser)(67%). Thus, “Sad” can be represented as AU variant [4, 15, 1 (60%), 6(50%), 11 (26%), 17 (67%)]. The expression “Fearful” can correspond toAUs: 1 (Inner Brow Raiser) (100%), 4 (Brow Lowerer) (100%), 20 (Lipstretcher) (100%), 25 (Lips part) (100%), 2 (Outer Brow Raiser) (57%), 5(Upper Lid Raiser) (63%), 26 (Jaw Drop) (33%). Thus, “Fearful” can berepresented as AU variant [1, 4, 20, 25, 2 (57%), 5 (63%), 26 (33%)].The expression “Angry” can correspond to AUs: 4 (Brow Lowerer) (100/a),7 (Lid Tightener) (100%), 24 (Lip Pressor) (100%), 10 (Upper Lip Raiser)(26%), 17 (Chin Raiser) (52%), 23 (Lip Tightener) (29%). Thus, “Angry”can be represented as AU variant [4, 7, 24, 10 (26%), 17 (52%), 23(29%)]. The expression “Surprised” can correspond to AUs: 1 (Inner BrowRaiser) (100%), 2 (Outer Brow Raiser) (100%), 25 (Lips part) (100%), 26(Jaw Drop) (100%), 5 (Upper Lid Raiser) (66%). Thus, “Surprised” can berepresented as AU variant [1, 2, 25, 26, 5 (66%)]. The expression“Disgusted” can correspond to AUs: 9 (Nose Wrinkler) (100%), 10 (UpperLip Raiser) (100%), 17 (Chin Raiser) (100%), 4 (Brow Lowerer) (31%), 24(Lip Pressor) (26%). Thus, “Disgusted” can be represented as AU variant[9, 10, 17, 4 (31%), 24 (26%)]. Accordingly, by knowing the AUs and/orAU variants used for a particular animation, the Facial Solver canidentify one or more emotions associated with that set of AUs and/or AUvariants.

Although many of the AU variants may be based on FACS, some expressionsmay be different from traditional FACS groupings. For example, thedisclosed FACS rig system may utilize different AUs or differentintensity scales to represent an emotion. Accordingly, the facial rigparameters 1204, 1208 can be coarsely mapped in real time. For example,if a real person smiles, the Facial Solver may interpret this as happy,and the person's avatar can be manipulated as a “happy” category (ratherthan point-to-point matching of the real person). This may simplify thecomputational load of the system.

Examples of Facial Subregions and Boundaries

The Facial Solver can iteratively calculate a correspondence betweenData 1 (e.g., input data) to Data 2 (e.g., rig data) by iterativelyminimizing an error metric over an ordered set of subregions. In manycases, the minimizations of the subregions work in a sequence that ismutually constitutive. For example, the minimizations for differentsubregions generally do not execute simultaneously. Instead, the resultsfor the minimization for one subregion can feed as input into theminimization for another subregion using a particular ordered sequenceof the subregions. The ordering of the sequence of the subregions may bereferred to as the “Solver Order.”

As described herein, the Solver Order typically begins with a largesubregion and moves to progressively smaller subregions until the entireregion has been solved. For example, a large subregion can correspond toa large area of movement, such as the jaw, or can correspond to a regionthat includes more facial muscles than other regions. In contrast, asmall subregion can correspond to a smaller region than a large regionand may represent more granular details, such as the eyes, or cancorrespond to a region that includes fewer facial muscles than otherregions. In other implementations, a large subregion may have a largersurface area (e.g., greater than 50 to 100 cm²) than a small subregion(e.g., less than 10 to 20 cm²), and the Solver Order may rank thesubregions by their respective surface areas (with subregions havinglarger surface area processed first, proceeding to subregions havingprogressively smaller surface area).

In other implementations, the ordering of subregions in the Solver Ordercan be based on a measure of the magnitude of deformation vectors ineach of the subregions. For example, if the magnitude of the set ofvectors is large (e.g., representing larger facial movements) then thecorresponding region would be processed earlier in the Solver Order thana subregion having a smaller magnitude (e.g., representing smallerfacial movements). As an example, if the magnitudes of the deformationvectors from the jaw subregion are largest, then the jaw subregion wouldbe classified as the first item in the Solver Order. If the rotation ofthe eyeball produces the lowest magnitudes of the deformation vectors,then the eyeball region would be classified as the final subregion inthe Solver Order. Thus, vector fields or flow fields representing thefacial deformation can be used to train or classify an ordered set ofsubregions for the Facial Solver.

In some embodiments, the Facial Solver can begin with rig settings in aneutral position, which can include a starting rig expression of thesubject. The starting rig expression can be referred to as a default rigand/or a neutral rig. For example, the starting rig expression caninclude a neutral expression in which all AUs are set to zero and/or allintensities are set to zero. Alternatively, the starting rig expressioncan include the settings for a previously determined facial rig (e.g.,for one or more subregions previously solved for in the Solver Order).The Facial Solver executes a minimization operation which iterativelyadjusts the FACS controls of a corresponding facial subregion to reduceor minimize an error metric between the input image and the particularfacial subregion of the facial rig. Further, the output of one iterationcan be the input of the next iteration. In this way, each iterationbuilds on itself and no iteration will be calculated using the samedata. The minimization operation for the subregion can terminate when atermination criterion is satisfied. For example, the terminationcriterion can be satisfied if the iteration executes a maximum allowednumber of iterations (e.g., 50 iterations). In addition oralternatively, the termination criterion can be satisfied when the errormetric (e.g., an L2 norm or a Hausdorff distance, as described below) isless than an error threshold (e.g., 0.01, 0.001, 0.0001), therebyindicating there is an acceptable match between the input image and thefacial rig for that particular subregion.

This process is repeated for each subsequent subregion, until all of thefacial subregion in the Solver Order have been solved for. As describedabove, the results for one subregion can be used as the initial guessfor the next subregion in the Solver Order. Thus, each subsequent facialsubregion may be easier to calculate, because only a portion of theinput image remains unsolved or unknown. Once all facial subregions havebeen solved for, the system sets the facial rig (e.g., AU) parameters asthe output of the Facial Solver. The facial rig parameters can be storedin non-transitory computer storage (e.g., non-volatile memory, optical,magnetic, or semiconductor storage).

A key frame in animation and filmmaking is a drawing that defines thestarting and ending points of any smooth transition. The drawings arecalled “frames” because their position in time is measured in frames ona strip of film. The facial rig parameters determined by the FacialSolver can be set as a frame or a key frame in an animation sequence.

In various implementations, subregions of the face are not only based onFACS, but are also based on biology (e.g., anatomy) of the face. Forexample, although FACS may have some groupings (such as groupscorresponding to the eyes or brow subregion), the disclosed subregionscan be based on the FACS groups but with modifications related tounderlying anatomy. For example, the Facial Solver can include one ormore subregions for one or more of a person's jaw, lower face, lips,upper face, eyelids, eyes, neck, lips, or tongue. In addition, theFacial Solver can include a subregion for a shape called the funneler.The funneler shape is within the lower face after the jaw moves butbefore the lips move.

FIG. 13 schematically illustrates example facial subregions andboundaries overlaid on the FACS rig of FIG. 12. The different facialsubregions are colored differently in FIG. 13 (e.g., the jaw subregion1304 is colored red, and the neck subregion is colored purple). Eachsubregion includes a collection of FACS parameters (e.g., AUs 1204 andAU directionalities 1208) of the facial rig. The results of eachsubregion feed into another subregion in the Solver Order. It is thisprocess of hierarchically compounding the results of each subregion andfeeding into the next subregion that allows the Facial Solver to achievea biologically motivated result that is qualitatively, quantitatively,and computationally robust.

Some approaches include a Facial Solver having a Solver Order that isbiologically ordered based on an impact of the muscles on the face. Forexample, in some cases, the sequence of priority in which the subregionsare processed by the Facial Solver is shown in Table 2.

TABLE 2 Solver Order 1) Jaw 2) Lower Face 3) Lips 4) Funneler 5) UpperFace 6) Lids 7) Eyes 8) Neck 9) Lip Extras 10)  Tongue 11) Miscellaneous Extras

The lower face subregion can comprise the nose and portions of thecheeks above the jaw line. The funneler can comprise a subregionsurrounding the lips, above the jaw, and below the lower face subregion.The upper face can comprise the forehead. The final subregion in theexample Solver Order, miscellaneous extras, may comprise fine-scalesubregions of the face such as temple veins, chin lines, small ear orcheek movements, etc.

In other words, with reference to the example in Table 2 and FIG. 13,first, the Facial Solver executes a minimization operation for the Jawsubregion, which iteratively minimizes an error metric between the jawin the input image and the jaw subregion 1304 of the facial rig. Next,the Facial Solver executes a minimization operation for the Lower Face,which iteratively minimizes an error metric between the lower face inthe input image and the lower face 1308 of the facial rig. The FacialSolver continues to successively apply minimization operations for eachof the remaining facial subregions in the Solver Order (e.g., endingwith miscellaneous extras) until all facial subregions have beenanalyzed. The parameters of the facial rig can be stored or used in ananimation sequence.

The jaw boundary 1304 can include the jaw's biomechanical parameters inrelation to FACS. Because the jaw or mandible subregion is the largestarticulating joint structure in the human face, movement of the jaw hasthe largest impact on the face when considering surface area of theface. Accordingly, the jaw boundary 1304 is closely related to speechand emotion. Therefore, in some cases, the jaw boundary 1304 has thehighest priority of the Solver Order, and the jaw parameters are thefirst to be solved for in the Solver Order (see, e.g., Table 2).

Although the lower face and mouth are both well connected to the jaw,the lower face covers a larger surface area than the mouth. Accordingly,the lower face boundary 1308 may be more closely related to speech andemotion than a mouth boundary (e.g., funneler 1312, lips 1316, lipextras 1336, tongue 1340, etc.). Thus, in some cases, the lower face canbe assigned the second priority (e.g., the second subregion to beexecuted). As described above, when solving for the second subregion,the Facial Solver can receive as input, the output of the results fromthe solution for the first subregion. Accordingly, in this example, thelower face boundary subregion receives, as input, the output of the jawboundary solution, and lower face boundary subregion can then beexecuted to further minimize an error metric between the input image andthe facial rig. The Facial Solver can proceed to the next item in theSolver Order, and will repeat this process for all remaining subregionsuntil the entire facial rig is solved.

Subregions in the Solver Order may not only be based on biology, butalso may be based on FACS. For example, in some cases, there is afunneler subregion. The funneler can include the lower face after jawmoves but before the lips move. Some implementations use the FACStaxonomy (for example, eyes or brow subregions of the FACS taxonomy).However, in some cases, the subregions are based at least partly on FACSgroups (e.g., some or all of the groups listed in Table 1) but withmodifications related to underlying anatomy. In other cases, otherfacial taxonomy systems can be used to determine the facial subregionsused in the Solver Order.

Example Facial Solver Variations

In some cases, the Facial Solver can include the same subregionsidentified above in Table 2 (e.g., Jaw, Lower Face, Lips, Funneler,Upper Face, Lids, Eyes, Neck, Lip Extras, Tongue, and Misc. Extras), butthe Solver Order may be different. For example, the Solver Order mayflow from largest impact to lower impact, lowest impact to highestimpact, largest group to smallest group, smallest group to largestgroup, or various other combinations. As a non-limiting example, in somecases, a Solver Order can prioritize lips over the jaw. In other words,a Facial Solver can solve for a lips region prior to solving for a jawregion. However, because lips can be the result of jaw movement, aFacial Solver that prioritizes lips over the jaw may be less accurate,more time consuming, and more challenging to implement than a FacialSolver that prioritizes the jaw over the lips. Accordingly, in somecases, the lips can be calculated at any point in the Solver Order,after the jaw. This may have the benefit of improving accuracy orreducing computation time (i.e. the number of iterations required toconverge).

In some cases, the Facial Solver can include the same subregionsidentified above, as well as the same Solver Order, but parameters persubregion are different. For example, in some cases, some of theparameters of the cheek could be in the jaw group. However, this maycause the cheek to take precedence over the jaw and popping may occur.Thus, it may not be advantageous to have a jaw parameter in a cheeksubregion, as it might cause the Facial Solver to be less accurate.

In some cases, the Facial Solver has the same Solver Order as identifiedabove and the same subregions, but with one or more of the subregionsfurther divided. For example, the subregions can be divided such that nosubregion has more than a particular number (e.g., 5) of parameters.However, in some cases, the result of this division of subregions mayappear disjointed because each of the parameters in the subregions workin conjunction with each other. Consequently, if the subregions arefurther divided, the avatar might appear jerkier during animation.

For each subregion of the Solver Order, the system can temporallyoptimize the numerical values for the facial parameters of the facialrig. These parameters may be based on the FACS parameters such as FACSAUs. For example, using the output from a previous subregion as theinitial prediction in the next subregion, the system creates aprediction loop. In some cases, this prediction loop can serve tostabilize any high frequency artifacts and maintain a lowercomputational complexity.

The system may utilize one or more various Facial Solver techniques forimproving or optimizing the numerical values for the facial parametersof the facial rig. For example, the system can minimize an error metric(described below) between the input image for a subregion (e.g., asdigitally represented by a point cloud or mesh) and the facial rig forthat subregion. During the optimization, facial rig parameter values areadjusted so that the error metric is reduced and ultimately converges bymeeting a convergence criterion. The convergence criterion can comprisethe error metric being reduced to below an error threshold (e.g., 0.001,0.0001, etc.) or a maximum number of iterations being performed duringthe optimization (e.g., 50, 75, 100 iterations).

The Facial Solver may be configured to utilize one or more optimizationtechniques. For example, Constrained Optimization By LinearApproximation (COBYLA) may be utilized, because this technique does notrequire knowledge of the derivative for the error metric. In addition oralternatively, the system can minimize the error metric using theNelder-Mead method (sometimes referred to as Simplex), theLevenberg-Marquardt algorithm (sometimes referred to as Least Squares),or the Newton Conjugate Gradient method. In some cases, the FacialSolver defaults to using COBYLA, but if COBYLA does not provide asolution, the system may re-run the Facial Solver with any of theforegoing alternative optimization methods to try to obtain a solution.In some cases, the choice of Facial Solver technique may beuser-selectable.

Example Error Metrics

The particular error metric utilized by the Facial Solver can be basedat least in part on the properties or characteristics of the input data(e.g., the input image). For example, if there is a topologicalcorrespondence (e.g., there is some vertex to vertex matching) betweenthe input data and the facial rig deformations, then the Facial Solvercan utilize a Euclidean method to measure the L2 (Euclidean) norm (alsoknown as least squares error (LSE)), for example, using Equation 1,below. In Equation 1, d(X,Y) is the L2 norm between a set of pointsX=(x₁, x₂, . . . , x_(n)) and Y=(y₁, y₂, . . . , y_(n)). In the FacialSolver, the point Y may represent the image data in a subregion, and thepoint X may represent the facial rig data for that subregion. The numbern may represent the number of vertices in the rig for the subregion.

d(X,Y)=√{square root over ((x ₁ −y ₁)²+(x ₂ −y ₂)²+ . . . +(x _(i) −y_(i))²+ . . . +(x _(n) −y _(n))²)}  (Equation 1)

There may be a topological correspondence between the input data and rigdata when the input data corresponds to mesh input (rather than anunstructured point cloud). This is because the mesh input may becorrelated to the facial rig deformations. An example of this caninclude input data corresponding to real-time user image capture with awebcam.

Further, the system can utilize the Euclidean L2 norm when moving fromone rig to a different rig. For instance, a more advanced rig canincrease the range of emotions the avatar can express. Thus, instead ofstarting from scratch, embodiments of the Facial Solver can utilize theEuclidean error metric to convert original rig values to the new rigvalues. For example, the Euclidean method can measure a distance betweena vertex of the input data relative to a vertex of the facial rig.

Although the L2 norm is commonly used as an error metric foroptimization problems, other norms can be used such as, e.g., L-p norms(the L2 norm is a sub-case with p=2), a Mahalanobis distance, and soforth.

In contrast, where there is no topological correspondence (e.g., novertex to vertex matching) between the input data and the facial rigdeformations, the Facial Solver can use the Hausdorff distance d_(H)(e.g., as illustrated in Equation 2 below) as the error metric function.In Equation 2, sup represents the supremum, inf represents the infimum,max represents the maximum, and d(x,y) represents a metric distance. TheHausdorff distance represents the greatest of all the distances from apoint in the set X (e.g., the facial rig) to the closest point in theset Y (e.g., the input image data).

$\begin{matrix}{{d_{H}\left( {X,Y} \right)} = {\max \left\{ {{\sup\limits_{x \in X}\inf\limits_{y \in Y}{d\left( {x,y} \right)}},{\sup\limits_{y \in Y}\inf\limits_{x \in X}{d\left( {x,y} \right)}}} \right\}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

For example, the system can utilize the Hausdorff distance when theinput data is unstructured point cloud input. This is because it is notalways clear what points in the unstructured point cloud input correlateto points in the facial rig. Using the Hausdorff distance, the systemcan obtain the closest point on surface (CPOS), and then the system cancalculate the Hausdorff distance by doing shape matching inneighborhoods.

Example Facial Solver Implementation

FIG. 14 is a block diagram that illustrates an example Facial Solverprocess 1400 implemented by the system. The process 1400 can beperformed and implemented by one or more computing devices or processingsystems. For example, the process 1400 can be performed by a computingsystem associated with a photogrammetric capture stage configured toobtain image scans of the subject or a computing system that isassociated with the wearable system 200, such as the avatar processingand rendering system 690 described with reference to FIG. 6B, such asthe 3D model processing system 680.

At block 1402, the Facial Solver begins, receiving a face performanceinput (block 1432). The face performance input (sometimes referred to asa performance capture) can include a temporal sequence of someoneperforming a pose (e.g. a video of a person going from a neutralexpression to a smile). That sequence can be driven in real time afterthe system solved for that sequence and that sequence is stored onnon-transitory computer storage. In some cases, the face performanceinput includes a plurality of images corresponding to various facialposes of a human subject. For example, the face performance can includea large number of high quality digitized photographic scans of the humansubject performing various poses (each pose may be referred to as aframe). The scans may be combined to form a 3D image of the humansubject. The face performance input can include one or more frames, suchas one or more frames of a video or an image.

For each frame (e.g., facial pose) in the performance (e.g., set offacial poses), the Facial Solver iterates over a plurality of subregions(blocks 1410, 1436), and in each subregion of the face, the FacialSolver iteratively calculates a reduction or a minimum between the frame(e.g., input image) and rig data for that subregion. As describedherein, the subregions work in a sequence that is mutually constitutivein that the results of one subregion feed into another subregion.Further, the Facial Solver iterates over the subregions following aparticular sequence (e.g., the Solver Order, see the example in Table2). As the Facial Solver works on a particular subregion, the FacialSolver optimizes the numerical values (e.g., adjusting the facial rigparameter values; blocks 1412, 1414, 1416) of the facial subregion tofind a best (or improved) fit to the input image data. For example, thesystem can perform this optimization by reducing or minimizing an errormetric (e.g., an L2 norm or a Hausdorff distance), e.g., via a techniquesuch as COBYLA (see block 1418). The Facial Solver can apply aconvergence test (block 1420) to determine if the minimization for asubregion has converged (e.g., the error metric between the facial rigand the input image has decreased below an error threshold or a maximumnumber of iterations has been reached). If convergence has not beenreached, the facial rig parameters are updated (block 1434) and theminimization continues at block 1418. At block 1424, if convergence hasbeen reached for the subregion, the minimizer is terminated, therebyindicating there is an acceptable match between the particular facialsubregion and the input image.

At block 1436, the Facial Solver proceeds to the next subregion in theSolver Order. This process is repeated for each subsequent subregion inthe Solver Order until the entire frame (e.g., the entire facial region)is analyzed. As described above, the results from one subregion can befed into the next subregion in the Solver Order. Thus, each subsequentsubregion may be easier to calculate because only a portion of the inputfacial image remains unsolved or unknown.

At block 1426, once all facial subregions for a particular frame havebeen solved, the system sets the facial rig (e.g., FACS AU) parametersto the output of the Facial Solver and may key frame all facial rigparameters at the current frame in the performance (block 1428) or applythe parameters to the facial rig (block 1430). At block 1406, once aparticular frame is solved, the Facial Solver accesses the next frame inthe performance (e.g., another image of another pose of a human subject)and repeats the process. The process is repeated for each frame of theface performance until every frame in the face performance is analyzedat which point the Facial Solver is terminated (block 1438) and theFacial Solver output can be stored in non-transitory computer storage(block 1440). An avatar (corresponding to the finalized facial rig) canbe stored on any type of non-transitory memory, such as an optical ormagnetic hard disk. Thus, instead of running a Facial Solver every timeand/or in real time, the system can run the Facial Solver once and storethe results. In some cases, the system can label the determined facialrig parameters corresponding to an emotion or an expression (e.g.,“happy”, “surprised”, etc.).

FIG. 15 illustrates an example process 1500 for determining a facial rigfor an avatar. The process 1500 can be used for setting a facial rig foran avatar by iteratively fitting subregions of the face of the avatar todetermine facial parameters of the facial rig associated with asubregion of a pose of the subject. The process 1500 can be performedand implemented by one or more computing devices or processing systems.For example, the process 1500 can be performed by a computing systemassociated with a photogrammetric capture stage configured to obtainimage scans of the subject or a computing system that is associated withthe wearable system 200, such as the avatar processing and renderingsystem 690 described with reference to FIG. 6B, such as the 3D modelprocessing system 680.

At block 1502, the process 1500 accesses a facial image of the subject,who may be performing a pose in an animation sequence. The facial imagemay have been previously converted to a 3D digital representation of thepose, e.g., a structured or unstructured point cloud or a mesh ofvertices. In some implementations, the process 1500 may (optionally)acquire 2D image data (e.g., from a photogrammetric stage) and calculatethe 3D digital representation (sometimes called a 3D scan) of thesubject using, e.g., 3D scanning, triangulation, or computer visiontechniques.

At block 1504, the process 1500 performs an embodiment of the FacialSolver described herein (see, e.g., the process 1400 described withreference to FIG. 14). For example, the process 1500 may iterate over aplurality of subregions of the face according to a Solver Order (see,e.g., Table 2). In some cases, the sequence of priority in the SolverOrder (e.g., first subregion to be processed to last subregion to beprocessed) is: 1) Jaw, 2) Lower Face, 3) Lips, 4) Funneler, 5) UpperFace, 6) Lids, 7) Eyes, 8) Neck, 9) Lip Extras, 10) Tongue, and 11)Miscellaneous Extras. The process 1500 can execute an optimizationmethod to obtain the fit of the facial rig parameters for the subregionto the input facial image data. For example, the process 1500 may usethe COBYLA optimization technique as a default technique. If COBYLA doesnot provide a suitable fit, the process 1500 may use the Nelder-Meadmethod (sometimes referred to as Simplex), the Levenberg-Marquardtalgorithm (sometimes referred to as Least Squares), or the NewtonConjugate Gradient method. After the optimization for a first subregionis performed, the process 1500 continues to the next subregion, untilall of the subregions in the Solver Order have been analyzed and a fullfacial rig for the entire face has been produced.

At block 1506, the process 1500 stores the facial rig parameters thathave been determined by the Facial Solver to correspond to the inputfacial image. Thus, the process 1500 can generate a facial rigcorresponding to a particular facial expression or emotion of thesubject as shown in the input facial image (e.g., smiling, laughing,showing surprise, etc.). At optional block 1508, the process 1500 usesthe facial parameters to animate a virtual avatar using the facial rig.

Although human animators have used human judgement and personalizedtechniques to animate avatars, the various embodiments of the methodsdisclosed herein provide automated (or at least semi-automated)animation methods based on a computerized set of rules (see, e.g., theprocess 1400) that can be applied to digital scans of a subject.Although the result may be substantially the same (e.g., avataranimation), the computerized technique is performed differently than thehuman operator's methods, which rely on individual human judgement andexperience.

Example Software Code

The Appendix includes an example of computer programming pseudocode thatcan be used to implement an embodiment of the Facial Solver describedherein. An embodiment of the processes 1400, 1500 can be implemented atleast in part by the example code in the Appendix. The Appendix alsoincludes description of the software code. The disclosure of theAppendix is intended to illustrate an example implementation of variousfeatures of the Facial Solver technology and is not intended to limitthe scope of the technology. The Appendix is hereby incorporated byreference herein in its entirety so as to form a part of thisspecification.

Additional Aspects

In a first aspect, a system comprising: non-transitory storageconfigured to store image data representative of a subject performing apose and information relating to a plurality of facial subregions; and ahardware processor in communication with the non-transitory storage, thehardware processor programmed to: access the image data; for each facialsubregion in a solver order of facial subregions: reduce an error metricby adjusting a facial rig parameter for the facial subregion; andterminate and move to the next facial subregion in the solver order offacial subregions when a termination criterion is met; set a facial rigfor the avatar based at least in part on the facial rig parametersdetermined for each of the facial subregions.

In a second aspect, the system of aspect 1, wherein an output facial rigparameter from a first facial subregion in the solver order is used asan input facial rig parameter for a second, subsequent subregion in thesolver order.

In a third aspect, the system of aspect 1 or aspect 2, wherein to reducethe error metric, the hardware processor is programmed to use aconstrained optimization by linear approximation (COBYLA) technique.

In a fourth aspect, the system of any one of aspects 1 to 3, wherein thefacial subregions of the solver order comprise: (1) Jaw, (2) Lower Face,(3) Lips, (4) Funneler, (5) Upper Face, (6) Lids, (7) Eyes, (8) Neck,(9) Lip Extras, and (10) Tongue. In some of these aspects, the jawregion comprises a region corresponding to a jaw bone, the lower faceregion comprises a region corresponding to a nose and portions of cheeksabove a jaw line, the lips region comprises a region corresponding tolips, the funneler region comprises a region corresponding to an areacorresponding to the lips, a region above the jaw, and a region belowthe lower face region, the upper face region comprises a regioncorresponding to a forehead, the lids region comprises a regioncorresponding to one or more eyelids, the eyes region comprises a regioncorresponding to one or more eyes, the neck region comprises a regioncorresponding to a neck, the lip extras region comprises a regioncorresponding to temple veins, chin lines, or ear or cheek movements, orthe tongue region comprises a region corresponding to a tongue.

In a fifth aspect, the system of any one of aspects 1 to 4, wherein theplurality of subregions is based at least in part on facial actioncoding system (FACS) taxonomy.

In a sixth aspect, the system of 5, wherein the facial rig parametercomprises a FACS action unit (AU).

In a seventh aspect, the system of any one of aspects 1 to 6, whereinthe termination criterion comprises the error metric being reduced tobelow a threshold error or a maximum number of iterations beingachieved.

In an eighth aspect, the system of any one of aspects 1 to 7, whereinthe error metric comprises a minimization function in the form of an L2norm or a Hausdorff distance.

In a ninth aspect, the system of any one of aspects 1 to 7, wherein thesubject comprises a human being, a non-human animal, or a personifiedobject.

In a tenth aspect, the system of any one of aspects 1 to 9, wherein thehardware processor is further programmed to label the facial rigparameters as corresponding to an emotion or an expression.

In an eleventh aspect, a display device comprising: a display configuredto present a virtual image of the avatar to a user; and the system ofany one of aspects 1 to 10.

In a twelfth aspect, an avatar control system comprising: non-transitorystorage configured to store input data corresponding to an image of asubject; and a hardware processor in communication with thenon-transitory storage, the hardware processor programmed to: access theinput data corresponding to the image of the subject; successivelyexecute, according to a solver order, a plurality of orderedoptimizations that each iteratively adjust one or more facial rigparameters of a facial rig to match the input data until a terminationcriterion is met, wherein each of the plurality of ordered optimizationscorresponds to a different facial subregion of the facial rig, and eachoptimization adjusts only facial rig parameters of the facial subregionon which the optimization is performed; responsive to the execution ofeach of the ordered optimizations, output the facial rig for the avatarbased on the adjusted one or more facial rig parameters.

In a thirteenth aspect, avatar control system of aspect 12, wherein thesolver order comprises: (1) Jaw, (2) Lower Face, (3) Lips, (4) Funneler,(5) Upper Face, (6) Lids, (7) Eyes, (8) Neck, (9) Lip Extras, and (10)Tongue.

In a fourteenth aspect, avatar control system of aspect 12 or aspect 13,wherein the solver order comprises a first subregion of the face and asecond subregion of the face, the first subregion comprising a lowerarea of the face and the second subregion comprising an upper area ofthe face, the upper area smaller than the lower area.

In a fifteenth aspect, avatar control system of any of aspect 12-14,wherein at least one of the ordered optimizations comprises aconstrained optimization by linear approximation (COBYLA) technique.

In a sixteenth aspect, avatar control system of any of aspect 12-15,wherein the adjusted facial rig parameters comprise facial action codingsystem action units.

In a seventeenth aspect, a system comprising: non-transitory storageconfigured to store facial image data for a person; and a hardwareprocessor in communication with the non-transitory storage, the hardwareprocessor programmed to: iteratively adjust first facial rig parametersof a first facial subregion of a facial rig for an avatar of the personuntil a first termination criterion is met; iteratively adjust secondfacial rig parameters of a second facial subregion of the facial riguntil a second termination criterion is met; and set the facial rig forthe avatar of the person based at least in part on the adjusted firstfacial rig parameters and the adjusted second facial rig parameters.

In an eighteenth aspect, the system of aspect 17, wherein the firstfacial subregion comprises a jaw subregion, and the second facialsubregion comprises a lower face subregion, the lower face subregioncomprising the nose and not comprising the jaw subregion.

In a nineteenth aspect, the system of aspect 17 of aspect 18, whereinthe hardware processor is further programmed to: iteratively adjustthird facial rig parameters of a third facial subregion of the facialrig until a third termination criterion is met, and wherein setting thefacial rig for the avatar is further based at least in part on theadjusted third facial rig parameters.

In a twentieth aspect, the system of aspect 19, wherein the third facialsubregion comprises a lips subregion.

In a twenty-first aspect, the system of any of aspects 17-20, whereinthe hardware processor is further programmed to: iteratively adjustfourth facial rig parameters of a fourth facial subregion of the facialrig until a fourth termination criterion is met, and wherein setting thefacial rig for the avatar is further based at least in part on theadjusted fourth facial rig parameters.

In a twenty-second aspect, the system of aspect 21, wherein the fourthfacial subregion comprises an eyes subregion.

In a twenty-third aspect, the system of any of aspects 17-22, wherein atleast one of the first termination criterion or the second terminationcriterion comprises an error metric between the facial rig and thefacial image data being reduced below a threshold or a maximum number ofiterations being exceeded.

In a twenty-fourth aspect, the system of any of aspects 17-23, whereinto iteratively adjust facial rig parameters, the hardware processor isprogrammed to execute a constrained optimization algorithm.

In a twenty-fifth aspect, a method comprising: accessing first facialrig data for a first facial rig; for each of a plurality of facialsubregions: iteratively adjusting, for a first facial subregion of theplurality of facial subregions, second facial rig parameters of a secondrig to match the first facial rig data for the first facial subregion ofthe first rig; and terminating and proceeding to a second subregion ofthe plurality of facial subregions when a termination condition is met;and outputting the second facial rig parameters.

In a twenty-sixth aspect, the system of aspect 25, wherein iterativelyadjusting second facial rig parameters comprises solving an errorminimization problem for an error metric between the first facial rigparameters and the second facial rig parameters for the first subregion.

In a twenty-seventh aspect, the system of aspect 25 or aspect 26,wherein a number of the plurality of facial subregions is greater than 3and less than 14.

In a twenty-eighth aspect, the system of any of aspects 25-27, whereinthe first facial subregion comprises a jaw subregion, and the secondfacial subregion comprises a lower face subregion, the lower facesubregion comprising the nose and not comprising the jaw subregion.

In a twenty-ninth aspect, a method comprising: accessing image data fora region of a subject performing a pose; for each of a plurality ofsubregions of the region of the subject, fitting facial rig parametersto match at least a portion of the image data; and outputting a facialrig comprising the facial rig parameters fit for each of the pluralityof subregions.

In a thirtieth aspect, the method of aspect 29, wherein the region ofthe subject comprises the face of the subject.

In a thirty-first aspect, the method of aspect 29 or aspect 30, whereinthe subject comprises a human being.

In a thirty-second aspect, the method of any of aspects 29-31, whereinthe subregions comprise a jaw subregion, a lower face subregion, a lipssubregion, an upper face subregion, an eyelids subregion, and an eyessubregion.

In a thirty-third aspect, the method of any of aspects 29-32, whereinthe fitting is performed according to a solver order, the solver orderbased at least in part on an impact each subregion has on animation ofthe virtual avatar.

In a thirty-fourth aspect, the system of any one of aspects 1-10 oraspects 12-24, the display device of aspect 11, or the method of any oneof aspects 25-33 or 37-39, wherein subregions are processed according toa solver order, wherein the solver order is based at least in part onone or more of: sizes of the subregions, surface areas of thesubregions, amounts of muscles in the subregions, measures of facialdeformations in the subregions, or magnitudes of facial deformationvectors or flows for the subregions.

In a thirty-fifth aspect, a display device comprising: a displayconfigured to present a virtual image of an avatar to a user; and thesystem of any one of aspects 12-24 or 40-41.

In a thirty-sixth aspect, a display device comprising: a displayconfigured to present a virtual image of an avatar to a user; and ahardware processor comprising non-transitory memory, the hardwareprocessor programmed to perform the method of any one of aspects 25-33.

In a thirty-seventh aspect, a method comprising: accessing image datastored in non-transitory storage, the image data representative of asubject performing a pose; for each facial subregion in a solver orderof facial subregions: reducing an error metric by adjusting a facial rigparameter for the facial subregion; and terminating and moving to thenext facial subregion in the solver order of facial subregions when atermination criterion is met; and setting a facial rig for the avatarbased at least in part on the facial rig parameters determined for eachof the facial subregions.

In a thirty-eighth aspect, a method comprising: accessing input datacorresponding to the image of the subject; successively executing,according to a solver order, a plurality of ordered optimizations thateach iteratively adjust one or more facial rig parameters of a facialrig to match the input data until a termination criterion is met,wherein each of the plurality of ordered optimizations corresponds to adifferent facial subregion of the facial rig, and each optimizationadjusts only facial rig parameters of the facial subregion on which theoptimization is performed; and responsive to the execution of each ofthe ordered optimizations, outputting a facial rig for the avatar basedon the adjusted facial rig parameters.

In a thirty-ninth aspect, a method comprising: iteratively adjustingfirst facial rig parameters of a first facial subregion of a facial riguntil a first termination criterion is met; iteratively adjusting secondfacial rig parameters of a second facial subregion of a facial rig untila second termination criterion is met; and setting the facial rig for anavatar based at least in part on the adjusted first facial rigparameters and the adjusted second facial rig parameters.

In a fortieth aspect, a system comprising: non-transitory storageconfigured to facial rig data; and a hardware processor in communicationwith the non-transitory storage, the hardware processor programmed to:access the facial rig data for a first facial rig; for each of aplurality of facial subregions: iteratively adjust, for a first facialsubregion of the plurality of facial subregions, second facial rigparameters of a second rig to match the first facial rig data for thefirst facial subregion of the first rig; and terminate and proceed to asecond subregion of the plurality of facial subregions when atermination condition is met; and output the second facial rigparameters.

In a forty-first aspect, a system comprising: non-transitory storageconfigured to store image data representative of a subject performing apose and information relating to a plurality of facial subregions; and ahardware processor in communication with the non-transitory storage, thehardware processor programmed to: access the image data for a region ofthe subject performing the pose; for each of a plurality of subregionsof the region of the subject, fit facial rig parameters to match atleast a portion of the image data; and output a facial rig comprisingthe facial rig parameters fit for each of the plurality of subregions.

Other Considerations

Each of the processes, methods, and algorithms described herein and/ordepicted in the attached figures may be embodied in, and fully orpartially automated by, code modules executed by one or more physicalcomputing systems, hardware computer processors, application-specificcircuitry, and/or electronic hardware configured to execute specific andparticular computer instructions. For example, computing systems caninclude general purpose computers (e.g., servers) programmed withspecific computer instructions or special purpose computers, specialpurpose circuitry, and so forth. A code module may be compiled andlinked into an executable program, installed in a dynamic link library,or may be written in an interpreted programming language. In someimplementations, particular operations and methods may be performed bycircuitry that is specific to a given function.

Further, certain implementations of the functionality of the presentdisclosure are sufficiently mathematically, computationally, ortechnically complex that application-specific hardware or one or morephysical computing devices (utilizing appropriate specialized executableinstructions) may be necessary to perform the functionality, forexample, due to the volume or complexity of the calculations involved orto provide results substantially in real-time. For example, animationsor video may include many frames, with each frame having millions ofpixels, and specifically programmed computer hardware is necessary toprocess the video data to provide a desired image-processing task orapplication in a commercially reasonable amount of time.

Code modules or any type of data may be stored on any type ofnon-transitory computer-readable medium, such as physical computerstorage including hard drives, solid state memory, random access memory(RAM), read only memory (ROM), optical disc, volatile or non-volatilestorage, combinations of the same and/or the like. The methods andmodules (or data) may also be transmitted as generated data signals(e.g., as part of a carrier wave or other analog or digital propagatedsignal) on a variety of computer-readable transmission mediums,including wireless-based and wired/cable-based mediums, and may take avariety of forms (e.g., as part of a single or multiplexed analogsignal, or as multiple discrete digital packets or frames). The resultsof the disclosed processes or process steps may be stored, persistentlyor otherwise, in any type of non-transitory, tangible computer storageor may be communicated via a computer-readable transmission medium.

Any processes, blocks, states, steps, or functionalities in flowdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing code modules, segments, orportions of code which include one or more executable instructions forimplementing specific functions (e.g., logical or arithmetical) or stepsin the process. The various processes, blocks, states, steps, orfunctionalities can be combined, rearranged, added to, deleted from,modified, or otherwise changed from the illustrative examples providedherein. In some embodiments, additional or different computing systemsor code modules may perform some or all of the functionalities describedherein. The methods and processes described herein are also not limitedto any particular sequence, and the blocks, steps, or states relatingthereto can be performed in other sequences that are appropriate, forexample, in serial, in parallel, or in some other manner. Tasks orevents may be added to or removed from the disclosed exampleembodiments. Moreover, the separation of various system components inthe implementations described herein is for illustrative purposes andshould not be understood as requiring such separation in allimplementations. It should be understood that the described programcomponents, methods, and systems can generally be integrated together ina single computer product or packaged into multiple computer products.Many implementation variations are possible.

The processes, methods, and systems may be implemented in a network (ordistributed) computing environment. Network environments includeenterprise-wide computer networks, intranets, local area networks (LAN),wide area networks (WAN), personal area networks (PAN), cloud computingnetworks, crowd-sourced computing networks, the Internet, and the WorldWide Web. The network may be a wired or a wireless network or any othertype of communication network.

The systems and methods of the disclosure each have several innovativeaspects, no single one of which is solely responsible or required forthe desirable attributes disclosed herein. The various features andprocesses described above may be used independently of one another, ormay be combined in various ways. All possible combinations andsubcombinations are intended to fall within the scope of thisdisclosure. Various modifications to the implementations described inthis disclosure may be readily apparent to those skilled in the art, andthe generic principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the claims are not intended to be limited to theimplementations shown herein, but are to be accorded the widest scopeconsistent with this disclosure, the principles and the novel featuresdisclosed herein.

Certain features that are described in this specification in the contextof separate implementations also can be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation also can be implemented inmultiple implementations separately or in any suitable subcombination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination. No single feature orgroup of features is necessary or indispensable to each and everyembodiment.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. In addition, thearticles “a,” “an” d “the” as used in this application and the appendedclaims are to be construed to mean “one or more” or “at least one”unless specified otherwise.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover. A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y and atleast one of Z to each be present.

Similarly, while operations may be depicted in the drawings in aparticular order, it is to be recognized that such operations need notbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Further, the drawings may schematically depict one more exampleprocesses in the form of a flowchart. However, other operations that arenot depicted can be incorporated in the example methods and processesthat are schematically illustrated. For example, one or more additionaloperations can be performed before, after, simultaneously, or betweenany of the illustrated operations. Additionally, the operations may berearranged or reordered in other implementations. In certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts. Additionally, other implementations are within the scope ofthe following claims. In some cases, the actions recited in the claimscan be performed in a different order and still achieve desirableresults.

APPENDIX

A portion of the disclosure of this Appendix contains material which issubject to copyright protection. The copyright owner has no objection tothe facsimile reproduction by anyone of the patent document or thepatent disclosure (which includes this Appendix), as it appears in thePatent and Trademark Office patent file or records, but otherwisereserves all copyright rights whatsoever.

The following computer pseudocode and description are intended toillustrate various examples of the facial animation technology but arenot intended to limit the scope of the technology.

Example Pseudo Code:

The following is an example of what an embodiment of the facialanimation code does from the time it accesses or receives a series offrames (e.g., images of an animation sequence of a subject) until theavatar for that subject is animated (e.g., facial structures are movedto animate the sequence).

1) init our Solver class : nameSpace=None, baseMesh=None,targetMesh=None, segmentation=None, debug=True 2) set our Solver args :region=None, SolverType=‘cobyla’, sampleCurrent=True, tol=1.e−7,startFrame=None, endFrame=None, step=1, subStep=1, preRoll=5, CPOS=FalseArguments: region: key from the Solver.order dict or a user specifiedlist of controls SolverType:  leastsq: Levenberg-Marquardt (default) nelder: Nelder-Mead  cg: Conjugate-Gradient  cobyla: CobylasampleCurrent: build the value/prediction array from the current frameif off uses previous frames as the subsequent prediction tol: Solvertolerence startFrame: start frame endFrame: end frame step: controls thetimestep through the timeline subStep: 0.5 does one subframe preRoll:number of frames for a runup 3) For subregion in Solver Order: for i inxrange(int(startFrame−preRoll), int((int(endFrame) + 1)/subStep), step):Set our current time step Build the parameter data from the Solver orderIf start frame: Initialize the prediction array Else: Use previousframes output as initial prediction array minimize the distance errorbetween the input mesh and facial rig. Set facial rig parameters to theoutput of the Solver and keyframe frame. Set prediction array to theoutput of the Solver. 4) Output solved facial rig parameter data tonon-transitory storage (e.g., disk) whether the system is solving for ananimation sequence or a single frame of an emotional state or facialpose.

Example Solver Order Groups

jaw = [ ‘Cn_jawStick_ctrl’, ‘Cn_jaw_thrustBack_ctrl’, ‘Lf_jawLine_ctrl’,‘Rt_jawLine_ctrl’] lowerface = [ ‘Lf_cheekRaiser_ctrl’,‘Rt_checkRaiser_ctrl’, ‘Lf_upperLipRaiser_ctrl’,‘Rt_upperLipRaiser_ctrl’, ‘Lf_noseWrinkler_ctrl’,‘Rt_noseWrinkler_ctrl’, ‘Lf_lipCornerDepressor_ctrl’,‘Rt_lipCornerDeprcssor_ctrl’, ‘Lf_lipDepressor_ctrl’,‘Rt_lipDepressor_ctrl’, ‘Lf_lipCornerPuller_ctrl’,‘Rt_lipCornerPuller_ctrl’, ‘Cn_chinRaiser_ctrl’,‘Lf_neckTightener_ctrl’, ‘Rt_neckTightener_ctrl·, ‘Lf_deepener_ctrl’,‘Rt_deepener_ctrl’, ‘Lf_lipStretcher_ctrl’, ‘Rt_lipStretcher_ctrl’,‘Lf_sharpLipPuller_ctrl’, ‘Rt_sharpLipPuller_ctrl’, ‘Lf_dimpler_ctrl’,‘Rt_dimpler_ctrl’] lowerface_old = [ Lf_lipDepressor_ctrl’,‘Rt_lipDepressor_ctrl’, ‘Cn_chinRaiser_ctrl’, ‘Lf_lipCornerDepressor_ctrl’, ‘Rt_lipCornerDepressor_ctrl’, ‘Lf_lipCornerPuller_ctrl’,‘Rt_lipCornerPuller_ctrl’, ‘Lf_upperLipRaiser_ctrl’,‘Rt_upperLipRaiser_ctrl’, ‘Lf_sharpLipPuller_ctrl’,‘Rt_sharpLipPuller_ctrl’, ‘Cn_lipsToward_ctrl’, ‘Lf_noseWrinkler_ctrl’,‘Rt_noseWrinkler_ctrl’, ‘Lf_dimpler_ctrl’, ‘Rt_dimpler_ctrl’,‘Lf_lipStretcher_ctrl’, ‘Rt_lipStretcher_ctrl’, ‘Lf_cheekRaiser_ctrl’,‘Rt_cheekRaiser_ctrl’] funneler = [ ‘LfUp_lipPucker_ctrl’,‘RtUp_lipPucker_ctrl’, ‘LfDn_lipPucker_ctrl’, ‘RtDn_lipPucker_ctrl’,‘LfUp_flatPucker_ctrl’, ‘RtUp_flatPucker_ctrl’, ‘LfDn_flatPucker_ctrl’,‘RtDn_flatPucker_ctrl’, ‘LfUp_lipFunneler_ctrl’,‘RtUp_lipFunneler_ctrl’, ‘LfDn_lipFunneler_ctrl’,‘RtDn_lipFunneler_ctrl’] lips = [ ‘Lf_upperLipDepressor_ctrl’,‘Rt_upperLipDepressor_ctrl’, ‘Lf_lipCorner_openClose_ctrl’,‘Rt_lipCorner_openClose_ctrl’, ‘Lf_lipPressor_ctrl’,‘Rt_lipPressor_ctrl’, ‘Rt_lipCornerThick_ctrl’,‘Lf_lipCornerThick_ctrl’, ‘Dn_lipsFlat_ctrl’, ‘Up_lipsFlat_ctrl’,‘Lf_nose_flareCompress_ctrl’, ‘Rt_nose_flareCompress_ctrl’] lips_old = [‘Lf_upperLipDepressor_ctrl’, ‘Rt_upperLipDepressor_ctrl’,‘Lf_neckTightener_ctrl’, ‘Rt_neckTightener_ctrl’,‘Lf_lipCorner_openClose_ctrl’, ‘Rt_lipCorner_openClose_ctrl’,‘Lf_nose_flareCompress_ctrl’, ‘Rt_nose_flareCompress_ctrl’,‘Lf_deepener_ctrl’, ‘Rt_deepener_ctrl’, ‘Lf_lipPressor_ctrl’,‘Rt_lipPressor_ctrl’, ‘Rt_lipCornerThick_ctrl’,‘Lf_lipCornerThick_ctrl’, ‘Dn_lipsFlat_ctrl’, ‘Up_lipsFlat_ctrl’,‘Lf_lipSticky_ctrl’, ‘Rt_lipSticky_ctrl’] upperface = [‘Rt_innerBrowRaiser_ctrl’, ‘Lf_innerBrowRaiser_ctrl’,‘Lf_browRaiser_ctrl’, ‘Rt_browRaiser_ctrl’, ‘Lf_browLower_ctrl’,‘Rt_browLower_ctrl’, ‘Lf_squint_ctrl’, ‘Rt_squint_ctrl’,‘Lf_lidTightener_ctrl’, ‘Rt_lidTightener_ctrl’] lids = [‘Rt_blink_ctrl’, ‘Lf_blink_ctrl’, ‘Lf_eye_closeOpen_ctrl’,‘Rt_eye_closeOpen_ctrl’, ‘Lf_lowerLidUpDn_ctrl’, ‘Rt_lowerLidUpDn_ctrl’,‘Lf_upperLidUp_ctrl’, ‘Rt_upperLidUp_ctrl’] eyes = [ ‘Lf_eyeStick_ctrl’,‘Rt_eyeStick_ctrl’] neck = [ ‘Cn_vocal_ctrl’, ‘Cn_swallow_ctrl’,‘Cn_apple_ctrl’, ‘Lf_platysmaA_ctrl’, ‘Lf_platysmaB_ctrl’,‘Rt_platysmaB_ctrl’, ‘Rt_platysmaA_ctrl’] lipsExtras = [‘Lf_lipSticky_ctrl’, ‘Rt_lipSticky_ctrl’, ‘Rt_upperLipMid_inOut_ctrl’,‘Lf_upperLipMid_inOut_ctrl’, ‘Rt_lowerLipMid_inOut_ctrl’,‘Lf_lowerLipMid_inOut_ctrl’, ‘Cn_upperLipBack_ctrl’,‘Cn_lowerLipBack_ctrl’, ‘Dn_lipsToward_ctrl’, ‘Up_lipsToward_ctrl’,‘lipBiteLower_ctrl’, ‘Dn_lipSuck_ctrl’, ‘Up_lipSuck_ctrl’,‘Up_lipTightener_ctrl’, ‘Dn_lipPuff_ctrl’, ‘Up_lipPuff_ctrl’,‘Cn_blow_ctrl’, ‘Dn_lipInner_partToward_ctrl’,‘Up_lipInner_partToward_ctrl’, ‘Dn_lipsThin_ctrl’, ‘Up_lipsThin_ctrl’,‘Dn_lipCurl_ctrl’, ‘Up_lipCurl_ctrl’, ‘Dn_lipTightener_ctrl’,‘Cn_muzzle_leftRight_ctrl’] tongue = [ ‘Cn_tongueBulge_ctrl’,‘Cn_topteeth_ctrl’, ‘Cn_botteeth_ctrl’, ‘Cn_tongue6_fk _ctrl’,‘Cn_tongue5_fk_ctrl’, ‘Cn_tongue4_fk _ctrl’, ‘Cn_tongue3_fk_ctrl’,‘Cn_tongue2_fk_ctrl’, ‘Cn_tongue1_fk_ctrl’, ‘Cn_tongue_ikh_ctrl’]miscExtras = [ ‘Cn_chinLine_ctrl’, ‘Cn_breathIn_ctrl’,‘Cn_templeVein_ctrl’, ‘Cn_neckVein_ctrl’, ‘Rt_earPin_ctrl’,‘Lf_earPin_ctrl’, ‘Rt_earUp_ctrl’, ‘Lf_earUp_ctrl’,‘Cn_gravity_inOut_ctrl’, ‘Rt_cheek_suckPuff_ctrl’,‘Lf_cheek_suckPuff_ctrl’, ‘Cn_gravity_ctrl’]

In some cases, “args” is arguments, “Lf” is left, “Rt” is right, and/or“Cn” is center.

1. An avatar control system comprising: non-transitory storageconfigured to store input data corresponding to an image of a subject;and a hardware processor in communication with the non-transitorystorage, the hardware processor programmed to: access the input datacorresponding to the image of the subject; successively execute,according to a solver order, a plurality of ordered optimizations thateach iteratively adjust one or more facial rig parameters of a facialrig for an avatar to match the input data until a termination criterionis met, wherein each of the plurality of ordered optimizationscorresponds to a different facial subregion of the facial rig, and eachoptimization adjusts only facial rig parameters of the facial subregionon which the optimization is performed; and responsive to the executionof each of the ordered optimizations, set the facial rig for the avatarbased at least in part on the adjusted one or more facial rigparameters.
 2. The avatar control system of claim 1, wherein the solverorder comprises: (1) a jaw region, (2) a lower face region, (3) a lipsregion, (4) a funneler region, (5) an upper face region, (6) a lidsregion, (7) an eyes region, (8) a neck regions, (9) a lip extras region,and (10) a tongue region.
 3. The avatar control system of claim 2,wherein the jaw region comprises a region corresponding to a jaw bone,the lower face region comprises a region corresponding to a nose andportions of cheeks above a jaw line, the lips region comprises a regioncorresponding to lips, the funneler region comprises a regioncorresponding to an area corresponding to the lips, a region above thejaw, and a region below the lower face region, the upper face regioncomprises a region corresponding to a forehead, the lids regioncomprises a region corresponding to one or more eyelids, the eyes regioncomprises a region corresponding to one or more eyes, the neck regioncomprises a region corresponding to a neck, the lip extras regioncomprises a region corresponding to temple veins, chin lines, or ear orcheek movements, or the tongue region comprises a region correspondingto a tongue.
 4. The avatar control system of claim 1, wherein the solverorder comprises a first subregion of a face and a second subregion ofthe face, the first subregion comprising a lower area of the face andthe second subregion comprising an upper area of the face, the upperarea smaller than the lower area.
 5. The avatar control system of claim1, wherein at least one of the ordered optimizations comprises aconstrained optimization by linear approximation (COBYLA) technique. 6.The avatar control system of claim 1, wherein the adjusted one or morefacial rig parameters comprise facial action coding system action units.7. The avatar control system of claim 1, wherein an output facial rigparameter from a first facial subregion in the solver order is used asan input facial rig parameter for a second, subsequent subregion in thesolver order.
 8. The avatar control system of claim 1, wherein to reducethe error metric, the hardware processor is programmed to use aconstrained optimization by linear approximation (COBYLA) technique. 9.The avatar control system of claim 1, wherein the plurality ofsubregions is based at least in part on facial action coding system(FACS) taxonomy.
 10. The avatar control system of claim 1, wherein thetermination criterion comprises the error metric being reduced to belowa threshold error or a maximum number of iterations being achieved. 11.The avatar control system of claim 1, wherein the error metric comprisesa minimization function in the form of an L2 norm or a Hausdorffdistance.
 12. The avatar control system of claim 1, wherein the subjectcomprises a human being, a non-human animal, or a personified object.13. The avatar control system of claim 1, wherein the hardware processoris further programmed to label the facial rig parameters ascorresponding to an emotion or an expression. 14-19. (canceled)
 20. Amethod comprising: accessing input data corresponding to the image ofthe subject; successively executing, according to a solver order, aplurality of ordered optimizations that each iteratively adjust one ormore facial rig parameters of a facial rig to match the input data untila termination criterion is met, wherein each of the plurality of orderedoptimizations corresponds to a different facial subregion of the facialrig, and each optimization adjusts only facial rig parameters of thefacial subregion on which the optimization is performed; and responsiveto the execution of each of the ordered optimizations, outputting afacial rig for the avatar based on the adjusted facial rig parameters.21-25. (canceled)